06 Postdoctoral Fellowship at University of Helsinki, Finland

The Data Science and Genetic Epidemiology Lab (https://www.dsgelab.org/) lead by Dr. Andrea Ganna is interested in the development or application of methods to early identify common preventable diseases. In our group, we integrate electronic health records and genetic information from large biobank-based studies (e.g. https://www.finngen.fi/en). Our team includes experts in machine learning, human genetics and epidemiology. Dr. Stefano Lombardi is senior researcher in the labor and education economics group of the VATT Institute for economic research. VATT is an internationally recognized research center founded in 1990 and located at the Economicum campus in central Helsinki together with Helsinki Graduate School of Economics (Aalto University, Hanken School of Economics, and University of Helsinki). Researchers at VATT evaluate the effects of policy measures on individuals, households and firms and promote evidence-based policy making. The main building blocks of VATT analyses are large-scale register data, profound institutional knowledge and economic and econometric theory.

The appointees shall hold an applicable doctoral degree and the ability to conduct independent scholarly work. The salaries for the positions are based on the salary system of Finnish universities. The salary range of a postdoctoral researcher is 3500-3700 euros/month depending on the appointees’ qualifications and experience. The initial contract duration for both positions is three years, or as agreed, with a possibility of extension. The positions will be filled with a 6-month trial period. We welcome applicants from a variety of genders, linguistic and cultural backgrounds, and minorities. The positions are opened in the HIP CMS experiment group which contributes to the analysis of the data from the CMS experiment and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and the Higgs sector, including the development of novel machine-learning methods for high-energy physics. Our experimental responsibilities include jet energy corrections, prompt calibration loop, and trigger activities.

The InversePDE project studies inverse problems for partial differential equations (PDEs) and geometric inverse problems. A typical inverse problem is the determination of the coefficient functions of a PDE from indirect data. The project aims to obtain unique solvability and stability results for inverse problems and to develop solution algorithms for these problems that are based on machine learning. The project will study inverse problems for linear and non-linear hyperbolic and elliptic PDEs by applying analysis, differential geometry, microlocal analysis, and probabilistic methods. The project will also combine methods of machine learning, in particular neural networks, and manifold learning techniques, with the mathematical theory of inverse problems to invent new algorithms that are rigorously guaranteed to work. We are looking for post-doctoral researchers interested in working on inverse problems and their applications. We also welcome applications from postdoctoral researchers who have not earlier worked on the above topics but are interested in extending their expertise to new fields. Women and other underrepresented groups in the field of mathematics are strongly encouraged to apply.

Our research group is part of the Academy of Finland’s Centre of Excellence in Tumour Genetics Research. The laboratory focuses on the study of transcriptional control of cell growth, using functional genomics and systems biology. Current lines of work include high-throughput determination of binding sites and binding specificities of human transcription factors, using this information to identify regulatory elements that control cell growth during normal development or in cancer.

As part of a new Academy of Finland project “Combining Coupled Modelling and Machine Learning to Constrain Antarctica’s Uncertain Future”, we are announcing a postdoctoral research scientist position at the Institute for Atmospheric and Earth System Research (INAR), University of Helsinki. The project is a collaboration between the Arctic Centre of the University of Lapland and INAR. The consortium leader is Professor John Moore. Our ICEMAP consortium will bring physically based modelling and Machine Learning (ML) together through Gaussian Process Emulation and Regression to more thoroughly explore factors influencing uncertain projections of Antarctica’s contribution to sea level than has been previously possible. The postdoctoral research scientist position involves ocean modelling in the Framework for Ice Sheet – Ocean Coupling (FISOC) with models such as ROMS and FVCOM and working on the use of emulation in building projections of land ice contributions to sea level rise. The postdoctoral researcher will assist in answering the following research questions: What is Antarctica’s projected contribution to sea level rise by 2300, including quantification of uncertainty? What are the main environmental contributors to uncertainty in Antarctica’s projected contribution to sea level rise by 2300? How effectively can ice shelf basal melt be parameterised without implementing an ocean model?

We are seeking an enthusiastic, highly motivated, well-organized and detail oriented postdoctoral researcher to study the resistance mechanisms to immunotherapy in head and neck squamous cell carcinoma (HNSCC) patients. The project is funded by the Academy of Finland. The selected applicant will have a contract with the University of Helsinki, however she/he will also need to be able to work in a newly established group at the Faculty of Health Sciences at the University of Eastern Finland, Kuopio campus. HNSCC represents a highly aggressive solid tumor, with a 5-year mortality rate of ~50%. Despite the emergence of targeted- and immunotherapies, the long-term prognosis of HNSCC patients remains poor. This project aims to improve patients´ responses to immunotherapy by revealing the resistance mechanisms to immunotherapy, identifying better treatment targets and predictive biomarkers, and introducing a personalized approach to test immunotherapeutics. More information about the principle investigator (Associate Professor Ahmed Al-Samadi) publications can be found here: https://pubmed.ncbi.nlm.nih.gov/?term=al-samadi+a&sort=date.