Postdoctoral Fellowship (38) at University of Copenhagen, Denmark

This postdoctoral project will develop general self-supervised and unsupervised machine-learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to build methods that use unlabelled experimental data together with differentiable simulations to generate realistic synthetic datasets, transferable representations, and robust downstream models without relying on large human-labelled datasets.

There is freedom to influence the project, and the exact direction of the project will be defined in dialogue with the candidate. Possible methodological directions include generative distribution matching, domain adaptation, diffusion- or score-based models for noise and style transfer, contrastive and masked self-supervised learning, uncertainty-aware learning, and few-shot adaptation.

The project is rooted in scientific machine learning, with biological imaging and biophysical simulation as driving testbeds. We work with data that range from chromatin organization to microorganism tracking. A strong candidate may come from machine learning, computer vision, generative modelling, scientific computing, simulation-based inference, or a related area.

Biogenic volatile organic compounds (VOCs) have a profound impact on local and regional climates. Yet, this impact is highly uncertain due to the lack of reliable estimates for natural emissions and uptake of these reactive volatiles. All organisms produce, and some consume, mixtures of diverse volatile compounds such as VOCs, reactive nitrogen gases (e.g., HONO), and sulfur/halogenated compounds (e.g., DMS and methylhalides). However, biological sources and sinks of volatiles, other than terrestrial plants, remain poorly characterized. In the Center for Volatile Interactions (VOLT), we focus on VOC production, consumption and transformations associated with soil, cryptogams, freshwater, seawater, and glacial ice environments, as well as plant leaf and rhizosphere microbial communities. Our research encompasses experimental (field and laboratory) and modelling work and includes e.g. rate measurements, experiments with stable isotopes, and molecular analyses to assess volatile production/consumption processes and identify the organisms involved. We offer creative and stimulating working conditions in a dynamic, collaborative, and international research environment. VOLT is a Center of Excellence housed in Universitetsparken, Copenhagen, where we have laboratory facilities and state-of-the-art instrumentation for analyses of volatile compounds, molecular analyses of microbial community structure and activity, as well as incubation and climate chamber facilities.

We are looking for a highly motivated candidate with a PhD in biology, chemistry or related fields, to conduct cutting-edge, hypothesis-driven research within the scientific area of VOLT, focusing on process understanding related to VOC exchange in terrestrial ecosystems. You have the opportunity to propose a research plan starting from your own skills and experience, working, for example, with leaf surfaces, tree stems, soil, or lake sediment.

The successful applicant will become part of the project entitled, GREENWATCH – Green Surveillance: Imagining a Sustainable Internet of Things, funded through a European Research Council Advanced Grant, 2025-2029. The project examines the potentials and challenges of employing the Internet of Things (IoT) as a communication system monitoring the effects of human activities of Earth’s ecosystems, which simultaneously entails surveillance of human individuals and social institutions. The position represents a unique opportunity to participate in interdisciplinary and culturally comparative empirical work covering China, Europe, and the United States, and to contribute to theory development on a strategic issue with global ramifications.

Current trends point to an increased use of plant resources in food products due to their supposedly lower environmental impact, while the global demand for milk and dairy products remains high because of their excellent nutritional and functional properties. A possible compromise for this dilemma could be hybrid products, i.e., novel foods that are comprised of both dairy and plant-based components, which could satisfy the nutritional and sensory needs of the consumers while also decreasing the environmental impact of the products.

The project particularly aims at valorising dairy industry side streams for developing novel plant-dairy hybrid foods, and special emphasis will be placed on the process optimisation and structural characterisation of the hybrid matrices.

The postdoc’s duties will include research and dissemination activities as well as teaching and supervision. As part of the BoostDairy project, the postdoc will work in close collaboration with a PhD candidate.

The GLOBE Institute at the University of Copenhagen invites applications for a postdoctoral position in bioinformatics and computational proteomics, based in the Institute’s palaeoproteomics and biomolecular archaeology group. The position serves as the computational core of StructProt, a cross-faculty UCPH Convergence project that combines protein biophysics, evolutionary genomics, biomolecular archaeology, and textile research to resolve species identity and post-translational modifications in the structural proteins collagen and keratin. The successful candidate will characterise collagen and keratin sequence diversity across thousands of published vertebrate genome assemblies, translate this into a structured reference resource for mass spectrometry species identification, and develop pipelines for the analysis of ZooMS (MALDI-TOF MS) and LC-MS/MS data. The work is led at GLOBE by Professor Matthew Collins in close collaboration with Assistant Professor Josefin Stiller (evolutionary genomics) and connects directly to the biophysical modelling of Professor Kresten Lindorff-Larsen. The position is for 21 months.

The postdoctoral position will primarily focus on research and outreach, although it may also include a limited amount of teaching. The distribution of responsibilities will be agreed upon before the position commences.

Each postdoc is expected to conduct individual research in close collaboration with the project group, consisting of early career and senior scholars, as well as external partners from both public and private sectors. The research will primarily rely on qualitative fieldwork, quantitative surveys and social scientific analysis. Additionally, the project will involve experimentation with the design of different form of engagement and democratic participation. The postdocs will be affiliated with world-leading research departments and contribute to researcher groups such as Nature, Environment and Climate (NEC) and Organization and Economy (OREO) at the Department of Anthropology, UCPH.

A 2-year postdoc position with the possibility of a 1-year extension is now open at University of Copenhagen shared between the sensory biology group lead by Assoc. Prof. Anders Garm, Marine Biological Section and the Centre for Gelatinous Plankton Ecology & Evolution, lead by Senior Scientist Dr. Cornelia Jaspers. The postdoc is funded via the Danish Research Council and the DFF2 project – Marine bioluminescence: How and why? The aim of this interdisciplinary project is to investigate the function and ecological significance of bioluminescence and the potential synthesis pathways of the light emitting substrate – coelenterazine. Even though bioluminescence is a surprisingly common trait in marine organisms, little is known about its ecological, evolutionary and biochemical nature. We will establish the invasive comb jelly Mnemiopsis leidyi, one of the very few animals known to produce coelenterazine, as model organism in our laboratories at University of Copenhagen. Apart from studying bioluminescence in wildtype animals, we aim to use CRISPRcas technology to produce knock-out lines, which will facilitate comparative physiological and ecological experiments to understand the cost, function and ecological significance of bioluminescence. We will also apply a range of advanced imaging techniques to visualize the light organs and their innervation. 

The successful applicant will be part of a highly interdisciplinary work environment spanning from biochemistry to marine ecology with a strong focus on experimental and hypothesis driven work. Importantly, a large part of the work will happen in close collaboration with the other postdoc included in the project. 

This project will investigate the therapeutic potential of Urocortin II (UCN2), a peptide hormone that signals through the CRHR2 receptor, to preserve muscle mass and function in disease and pharmacological weight-loss contexts. Your tasks would include designing and executing in vivo studies in models of diabetic myopathy and diet-induced obesity, performing comprehensive metabolic and skeletal muscle phenotyping, processing samples for transcriptomic profiling, conducting bioinformatic analyses of omics-based datasets, and preparing scientific manuscripts and presenting findings at national and international conferences. The fellowship will provide advanced training in translational metabolic physiology, pharmacological target validation, and multi-omics integration.

We are seeking a highly motivated and enthusiastic Postdoctoral Researcher to join a translational project in the Hansen group, bridging two major research initiatives in oral and liver health.
The project integrates human genetics, salivary microbiome and metaproteomics, and longitudinal clinical data to uncover causal mechanisms and biomarkers along the oral–liver axis. The project requires a computationally oriented Postdoctoral researcher with strong expertise in the analysis and integration of large-scale biological datasets. The candidate will primarily focus on computational analysis, multi-omics integration, and biological interpretation of complex datasets.

We value curiosity, openness, and strong personal relationships, and aim to foster an environment where early‑career researchers can grow independently while engaging closely with clinicians and method experts. In this collaborative context, the successful candidate will work with interdisciplinary teams to integrate expertise across oral health, liver disease, and multi‑omics research.

We are seeking a passionate Postdoctoral researcher to join our team to identify, characterize, and validate novel molecular mechanisms and/or modulators of AMP-activated protein kinase (AMPK). AMPK is a key regulator of cellular and whole-body energy homeostasis, and its pharmacological modulation represents a promising strategy for the treatment of metabolic diseases, including type 2 diabetes and fatty liver disease. The goal of this position is to advance fundamental understanding of cellular energy and metabolic regulation through AMPK signaling and explore potential therapeutic applications. The successful candidate will be embedded in a highly collaborative and interdisciplinary research environment with expertise spanning metabolic signaling, computational chemistry, in vivo physiology and pharmacology, and molecular and cellular biology. The project will focus on identifying novel molecular mediators and metabolic regulators of AMPK signaling pathways, using biochemical, multi-omics, and computational approaches, alongside established genetic models and chemical tools of AMPK. Particular emphasis will be placed on AMPK-mediated pathways, with a focus on skeletal muscle glucose uptake and glycogen metabolism, as well as hepatic glucose and lipid metabolism.

We are seeking a motivated Postdoctoral researcher to study the role of plasma protein in human metabolic health and disease. We strive for a collaborative, ambitious, and supportive environment where scientific rigor, curiosity, and personal development are valued. Your tasks would be to map the plasma proteome in human samples across health, obesity, insulin resistance, and type 2 diabetes; and validate selected candidates and investigate their functional roles using cell-based assays and animal models. The ideal candidate has experience in proteomics, metabolism, molecular biology, bioinformatics, or related areas, and is enthusiastic about translational research connecting human omics data to disease mechanisms.

As a Postdoctoral researcher, you will develop, implement, and apply state-of-the-art computational strategies for structure-based peptide design, leveraging recent advances in AI-based structure prediction and co-folding (e.g. AlphaFold-class methods), molecular modeling software such as Rosetta, and GPU-accelerated computing. You will build robust and scalable computational pipelines and drive the project scientifically and technically.

You will use these approaches to design peptide ligands targeting metabolic GPCRs, focusing on the neuropeptide Y (NPY) and MCH receptor families, integrating peptide design, structural filtering, and refinement, and work closely with experimental collaborators to translate designs into functional ligands through synthesis, pharmacology, and in vivo validation.

In parallel, you will extend the framework to identify receptor targets for bioactive peptides and generalize it to new systems.

We are seeking a motivated researcher with strong computational expertise to join an interdisciplinary team working at the interface of statistical genetics, machine learning, and cardiometabolic research. The position focuses on developing and applying scalable and rigorous approaches for genetic discovery of cardiometabolic outcomes using biobank-scale data. Your tasks will involve evaluating and applying computational and statistical methods for genetic association studies under complex data conditions, including extensive phenotype missingness and heterogeneity across biological or environmental contexts, such as age, sex, adiposity, or inflammatory state. You will work with large-scale population and biobank datasets from multiple international cohorts, comparing and integrating modern imputation, prediction, and surrogate-based inference approaches to support uncertainty-aware genetic discovery. Depending on your background and interests, you may contribute to methodological development, large-scale data analysis, simulation and benchmarking studies, and cross-cohort validation, including the integration of additional molecular data layers such as proteomics. The position offers close mentorship, strong peer support, and access to a broad international research network, with opportunities to collaborate across disciplines and to develop and independent scientific profile within an inclusive, international research environment. We strive to foster a supportive and inclusive work environment in which ideas are developed through close interaction, teamwork, and open scientific exchange, while encouraging intellectual ownership and professional development.

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoc in Central Control of Energy Homeostasis

Deadline : August 10, 2026,

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Much of the current civil society activity is digital, and most of it happens on private commercial platforms. The advertised position is part of Advotar, a research project on the core principles of sovereign data infrastructure which could support effective use of AI across the civic society, while maintaining institutional trust and control over the citizen’s data. The project team includes chief scientists with expertise in machine learning (Professor Serge Belongie, Pioneer Center for AI), natural language processing (Associate Professor Anna Rogers, IT University of Copenhagen) and political science (Professor Rebecca Adler-Nielsen, University of Copenhagen), as well as a postdoc in natural language processing (Terne Sasha Thorn Jakobsen, IT university of Copenhagen). We also collaborate with Os&Data, a Danish non-profit organization experimenting with alternative infrastructures for supporting the civil society. The ongoing work focuses on technical NLP research: what kind of AI infrastructure could enable citizens to effectively access the communal resources (both from the public institutions and civic communities)?

We are seeking a civic-minded researcher with human-computer interaction skills to join the group. A well-documented problem with the current AI systems is the lack of factuality, but little is done to consider interfaces that would encourage verification and contextualization. Another problem is that the current modes of interacting with AI for information access are rather ‘selfish’: the people who originally provided the knowledge used by the AI system, do not get recognition when their data helped someone else — and there is no incentive for the AI user to contribute back to the knowledge communities.

Social welfare policies are important structural determinants of childhood health. These policies, for example, influence the likelihood of experiencing economic hardship during childhood, a well-established risk factor for adverse health and developmental outcomes. By rigorously evaluating the effects of specific social welfare reforms, we can identify which policy mechanisms most effectively promote health and development in early life, with implications for lifelong well-being and socioeconomic outcomes.

This research project will examine the child health implications of major Danish social welfare reforms that have introduced substantial changes to the benefit system and affected many families. Using population-wide register data, we will investigate the impact of these reforms on pregnancy outcomes as well as children’s somatic and mental health.

The postdoctoral researcher will be part of an inclusive and stimulating academic environment, with strong expertise in health inequalities and quantitative methods, within the Social Epidemiology Research Group at the Section of Social Medicine.

Anthropogenic changes such as climate and land use change influence the emergence and risk of vector-borne diseases, which are transmitted to humans and animals via blood-feeding insects.

As Denmark accelerates its green transition to address biodiversity loss and climate adaptation, they may create favorable conditions for these disease‑transmitting organisms (vectors), such as mosquitoes, and thereby unintentionally increase vector‑borne disease risks. As part of a new interdisciplinary research project, to investigate the relationship between on-going land use changes (as part of Denmark’s Green tripartite) and the effect on mosquito populations in Denmark, we are seeking a highly motivated postdoc to work with environmental DNA (eDNA)-based mapping of mosquitoes in Denmark. The project will use eDNA metabarcoding analyses of water samples collected (through citizen science and other participatory science approaches) across different landscape types to map mosquito species diversity in Denmark and to evaluate eDNA as a tool for mosquito surveillance. For comparison with eDNA methods, traditional mosquito trapping and molecular species identification will be performed at selected sites. There will also be opportunities for developing own research ideas related to eDNA and vector borne diseases in collaboration with the group.

The position is available from October 2026 or as soon as possible thereafter. The position is for 2 years and will be one of two postdoc positions (the other postdoc will have a background in social science) in the newly started interdisciplinary project funded by the UCPH Convergence Grants. Project partners are Natural History Museum of Denmark and Department of Food and Resource Economics. The two postdocs are expected to work closely together in the project.

The overall purpose of this postdoctoral position is to research and build the technical infrastructure that ensures responsible AI remains fair, reliable, and safe when deployed in the real world — not just under controlled laboratory conditions. Possible topics include automated pipelines for continuous bias and fairness auditing, tools for bridging cutting-edge research models and Statistics Denmark’s secure infrastructure, and monitoring systems that detect model drift or performance degradation before these issues affect citizens or policy decisions. It is critical that this research addresses the practical deployment challenges of the collaboration with Statistics Denmark.

The research position is situated at the Danish National Research Foundation’s Center of Excellence for Global Mobility Law, MOBILE. We seek a strong candidate with research expertise in the economics of international migration and mobility law, with particular focus on advanced quantitative methods and the construction and harmonisation of large-scale datasets linking comparative legal perspectives and de facto migration and mobility. The position is part of MOBILE’s effort to expand its quantitative empirical capacity at the interface of law, transnational mobility, and economics and inequality.

The position is connected to MOBILE’s core grant from the Danish National Research Foundation, which established the Center of Excellence for Global Mobility Law to systematically study the legal infrastructures of human mobility across geographies, social divides, travel patterns and time. Within this broader research programme, the postdoc will be expected to pursue empirical research on the determinants and consequences of cross-border human mobility under different and shifting legal and policy regimes, working in an interdisciplinary team and contributing methods such as gravity models, causal inference designs, and regression-based decomposition techniques. The postdoc will also be able to pursue their own research ideas within the overarching scope of MOBILE’s research agenda.

The Danish National Research Foundation’s Center of Excellence for Global Mobility Law (MOBILE) is seeking a postdoctoral candidate with experience in the interplay between legal regimes in the adoption, implementation and enforcement of mobility law in South America. Mobility law refers to the body of legal rules and practices that govern cross-border human movement, including how people may enter, reside, move, and work across states. It spans different legal domains, including free movement, migration, transnational labour, refugee, and human rights frameworks.

This position is situated within MOBILE’s overarching research agenda, which analyses the legal processes through which regional infrastructures for cross-border human mobility are shaped and subsequently implemented, enforced, and contested in practice. Applicants are expected to conduct socio-legal research that empirically examines the development, implementation, and contestation of South American regional migration and free movement legal frameworks, particularly in their interaction with domestic and international legal regimes. The research will moreover contribute to the broader study of mobility systems across different regions. Hence, the candidate will be expected to closely engage in comparative and interdisciplinary collaborations with other staff at the MOBILE center.

The XHAILe project aims to provide efficient and accurate hybrid AI technologies for computational law and natural language interaction with e-government solutions along with trustworthy benchmarks and guidelines for legal use of the technology. The successful candidate is expected to conduct research in logic and symbolic AI for computational law and declarative process management to contribute to its application in the project.

The project will be building on research of the PI and his research groups jointly with the industry partner DCR Solutions, which is a spin-off from a prior research project, centred around the development of a graph-based modelling language, DCR Graphs, for the declarative modelling of law and business processes. The DCR Graphs language is today used widely and successfully in the public sector.

The XHAILe project aims to provide efficient and accurate hybrid AI technologies for computational law and natural language interaction with e-government solutions along with trustworthy benchmarks and guidelines for legal use of the technology.

This position focuses on the development of conversational AI systems for interaction with legal and administrative processes, combining large language models with symbolic rule-based systems. The overall goal is to enable reliable, explainable, and user-friendly access to complex legal procedures through natural language interfaces.

The work builds on the project’s hybrid architecture, where language models are used for interaction, interpretation, and explanation, while decision-making remains grounded in executable symbolic models (DCR graphs) developed within the project and by the industrial partner DCR Solutions.

The position is part of the “Power over Platforms?” project which started in 2025 and runs till 2028 and is funded by the Danish National Research Foundation. The candidate’s research activities will contribute to the project’s empirical studies of how different kinds of actors seek to influence how platform companies govern their products and services.

The aim of the project is to understand how power is exercised over platform companies such as Google, Meta, and their competitors by actors who have neither raw economic nor formal regulatory or legislative means. The focus is on how civil society groups, interest groups, professional associations, and companies from other sectors sometimes actively try to shape how platform companies operate. 

Systematically analyzing the actors involved in trying to influence key decisions made by platforms on key issues—content moderation, privacy, and the use of generative AI for political information and speech—across different jurisdictions (inside and outside the EU), and across different platforms (primarily consumer-facing content platforms including Google, Meta, and their smaller competitors such as TikTok and X) the project sets out to identify who seeks to influence platform governance, how, and what the outcomes are across countries and across companies.

We are seeking a postdoctoral researcher to develop biophysics-based strategies for designing protein binders. The project will use computational biology and molecular modelling, based on the physical principles of protein structure, dynamics, and molecular recognition, to create protein binders with well-defined and experimentally testable interaction properties. The work will be highly collaborative, linking computational design with experimental protein science and drawing on ideas from biophysics, protein engineering, chemistry, computer science, and biotechnology.

The appointment is for a period of one year. The candidate will participate in a larger, cross-disciplinary project aiming at designing screening pipelines for binders for proteins involved in cell wall synthesis in gram positive bacteria with the aim of combatting antimicrobial resistance. The position is part of a project group spanning three other postdocs, of which two are experimentalists that will screen and validate the designed binders. The last postdoc will focus on the IP related consideration related to AI designed proteins.

The successful candidate will work on our recently developed system which combines lineage tracing with single cell proteomics readouts. The candidate will use this system in the context of normal and malignant blood cell development with the ambition to identify protein-based signatures predicting HSC and LSC behaviour in relevant experimental systems – and subsequently experimentally explore relevant predictive driver proteins.

The project investigates molecular and nanoscale mechanisms regulating synaptic transmission and presynaptic plasticity using genetically accessible Drosophila model systems. Current work focuses on how release site organization, active zone dynamics, and neuromodulatory signaling regulate neurotransmitter release at identified synapses.

Cai Lab investigates how the neurovascular unit and neural circuits fail in brain disease, and how this knowledge can be translated into the development of new neurotechnologies and therapies.

Our research has two closely connected pillars. First, we study the cellular mechanisms of neurovascular dysfunction in the living brain, with a particular focus on neurovascular coupling, blood flow regulation, vascular cell signalling in health, ageing, and ischemic stroke/reperfusion injury. Second, we develop novel neuroprosthetic and neuromodulation strategies for brain disorders, such as next-generation implantable micromagnetic coil-based microdevices for vision restoration and epilepsy.

The ocean currently absorbs ~25% of annual anthropogenic carbon dioxide (CO2) emissions. This climate service has delayed atmospheric CO2 concentrations from exceeding 500 ppm, buffering global warming. However, this uptake alters the carbonate system, leading to ocean acidification (OA): CO2 reacts with seawater to form carbonic acid, increasing hydrogen ion concentration, lowering pH, and reducing carbonate ion availability. These shifts in carbonate chemistry have cascading consequences for marine organisms, ecosystems, and human societies. OA is no longer a distant threat; impacts are already evident, from disruptions to shellfish hatcheries to broader marine ecosystem change.

Single drivers physiological performance curves are essential for understanding how marine organisms respond to environmental stressors, particularly in the context of OA and its interactions with other drivers. SAFESEA will identify performance curves, critical tipping points, and/or thresholds where performance is most likely to decline or fail. The focus for the post doc will be on phytoplankton and micro-zooplankton with contrasting physiological traits (calcifying, photosynthetic, mixotrophy, symbiotic and heterotrophic). Performance curves will help to assess the optimal pH range for key traits, which can support improving models in the project, and will provide information on levels at which the susceptibility to other drivers might be enhanced. Experimental results and meta-analyses will be used to develop a theoretical framework that integrates performance curves for key physiological traits to predict marine organism responses to pH and other environmental drivers. By combining models and environmental datasets we will project organismal responses to different scenarios of future conditions and identify critical scenarios for experimental validation in later work. Building on the single-driver work, SAFESEA will evaluate the combined effects of multiple environmental drivers on marine organisms, by designing and conducting multi-driver experiments tailored to the specific sites and species studied in the single driver experiments.

You will work on everything from data collection over machine learning modelling to clinical implementation. Specific emphasis will be on the machine learning component achieving high performance, robust and usable solutions. Special care will be taken to the robustification via use of modern machine learning technologies including foundation models. You will work in a team of machine learning researchers in collaboration with clinical experts.

The transmembrane G protein-coupled receptors (GPCRs) take part in a myriad of physiological processes and are involved in numerous pathologies. The project aims to investigate the role of GPCR mechanosensation in GPCR pharmacology.

In the project will include the establishment of novel assays for the detection of the interplay between GPCR oligomerization and mechanosensation at the subcellular level, using a combination of fluorescence microscopy and live cell GPCR signalling assays.

The project will go deep to understand which mechanisms are at play when GPCR receptors respond to mechanical stress. A close collaboration with researchers from the Neils Bohr Institute and the Department of Chemistry will ensure access to the needed crossdisciplinarity expertise and microscopy equipment.

Our group works in the broad area of proteomics, genomics, epigenetics, and cell signaling. In this project, we are interested in understanding epigenetic mechanisms of gene regulation, with special focus to understand the mechanisms of gene activation by cis-regulatory enhancers. The project will use quantitative proteomics and genomics to further understand the role of acetylation and other chromatin modifications in gene regulation. The project will combine CRISPR-based genome editing with NGS-based approaches (i.e. RNA-seq, ChIP-seq, ATAC-seq) and cell biological approaches to uncover new regulatory pathways and decipher underlying mechanisms.

Postdoc candidate(s) with experience in wet laboratory work will perform wet laboratory experiments involving cultured mammalian cells, creating CRISPR engineered cell lines, generating NGS data, including high-quality ChIP-seq data. The candidates with experience in both wet lab and bioinformatics will be able to do both (i.e. work in the wet lab and perform bioinformatics analyses).

Our group studies human brain development as well as dysfunction in disease using tractable human induced pluripotent stem cell (iPSC) models. In this project, we aim to elucidate mechanisms of transcriptional variation in neural systems. Specifically, the project involves the generation of human neural cell types from human iPSCs, single-cell RNA sequencing and analyses, CRISPR screening, and downstream cell biological approaches.  The overall goal is to identify mechanisms which modulate transcript-level variability in neural cells and to manipulate variability to assess phenotypic impact. This project requires candidates to utilize a combination of both wet-lab and dry-lab approaches. The candidate will perform experiments in the laboratory, analyse and interpret data, prepare data for publication, as well as attend and present findings at seminars and conferences. If you would like to learn more about this or other projects in the lab, please contact us!

We are seeking a highly motivated and ambitious postdoctoral candidate with a strong research background in the field of cell biology, stem cell medicine, or similar. You will design and execute a phenotypic drug screen using primary human alveolar organoids as the model system. The screen will be set up and run at StemScreen, our state-of-the-art screening facility equipped with fully automated liquid handling (Hamilton) and high-content image analysis (Yokogawa). You will characterize hits mechanistically to advance our understanding of alveolar repair and to identify potential therapeutic targets to improve endogenous alveolar regeneration in diseases such as COPD.

The role of the PostDocs will be to develop research techniques for improved assessment and monitoring of woody vegetation ecosystem properties at the level of single trees based on relevant remote sensing technology and AI algorithms, with a focus on species mapping as well as disturbances and change dynamics of trees both inside and outside forests.

Specifically, the first position will work on advancing large-scale, remotely sensed mapping of forest carbon dynamics. The position focuses on integrating state-of-the-art satellite-derived biomass, forest structure, and land-use change products to improve spatially explicit estimates of forest carbon stocks and fluxes across multiple decades. Using data-driven modelling approaches, the postdoc will refine regional- to continental-scale carbon budget assessments and explore how forest management, natural disturbances, and climate extremes shape the long-term trajectory of the forest carbon sink of Europe and possibly beyond depending on data availability/quality. The position aims to produce independent, observation-based constraints on forest carbon dynamics that are directly relevant for climate mitigation policies, greenhouse gas reporting, and biodiversity conservation at the European level.

The position is open from October 15st 2026 or as soon as possible thereafter and will be for a duration of 36 months.

The second position focusses on developing next-generation methods for mapping tree species composition across European forests using satellite and aerial imagery. The work will exploit and benchmark state-of-the-art deep learning architectures, including convolutional neural networks, vision transformers, and foundation models. Multi-sensor data will be integrated to resolve challenging species mixtures and structural variability. The postdoc will design robust training pipelines that leverage weak supervision based on existing species maps, national forest inventory data, and targeted field plots. Particular emphasis will be placed on model generalisation across biogeographical regions and sensor types. The resulting high-resolution species maps will form a core layer for assessing forest vulnerability and resilience. By linking species-distribution patterns with observed pest outbreaks and drought events, the project will provide new insights into species-specific risk profiles under climate change. The postdoc will also contribute to open, reproducible workflows and uncertainty quantification to ensure that outputs are usable for policy, management, and impact modelling at the European scale.