Postdoctoral Fellowship (05) at Stockholm University, Sweden

The research project addresses the question of the origin of life on Earth by exploring the transition from prebiotic to biotic worlds. The project focuses on the critical role of organic-mineral interactions in abiotic conditions resembling early Archean Earth. A central aim is to define distinct criteria for organosignatures – organically influenced mineralization without a specific cell signature – and to differentiate them from biosignatures, which are unequivocal evidence of life. Through laboratory studies, this research aims to produce a ‘proof of concept’ that could change our view of Earth’s early history before the emergence of life and also be relevant for the search for extraterrestrial life.

The successful candidate will design and perform experiments that mimic early Earth conditions, focusing on the formation and characterization of prebiotic organic matter and its initial interactions with minerals. This role requires knowledge on synthetic organic chemistry advanced analytical skills, and a profound interest in the understanding of early Earth geological and geochemical processes.

The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study complex biological processes. This project combines timely analytical challenges with deep rooted applications in life science. We are looking for a candidate with a PhD in either engineering/computer science/physics/mathematics. Experience with ML implementation (ideally interpretable ML and/or generative AI) is required.

The Selected postdoctor will be joining the Computational Microscopy for Cell Biology (CMCB) lab led by Dr. J. Griffié, which is primarily composed of engineers, physicists and mathematicians. The lab is based at SciLifeLab in Stockholm, a vibrant and dynamic science hub, with a particular focus in multidisciplinary research. The CMCB laboratory aims more specifically at developing cutting-edge data/image analysis as well as modelling strategies to answer fundamental biology issues with advanced light microscopy data. The lab’s research scope ranges from reinforcement learning for drug design, interpretable ML pipelines for cancer research and diagnosis as well as graph neural networks for microscopy.

The candidate will work on the end-to-end modelling of magnetic field evolution from the early Universe to the present epoch. In particular, one postdoc will focus on the early Universe dynamics and the other on the dynamics during the recombination era. This is part of a joint effort within the ERC Synergy Grant COSMOMAG with Andrii Neronov (APC and Lausanne), Chiara Caprini (CERN and CNRS), and Franco Vazza (U Bologna). Additional postdoc and PhD positions will be advertised in the following years. Frequent visits between the nodes of COSMOMAG are to be envisaged for discussions and joint coordination of the work.

The COSMOMAG project aims at pinning down the origin of cosmological magnetic fields by developing a unified framework tackling the problem from the initial conditions in the early universe up to observations in the present universe: we will study the magnetic field generation in the early universe, use numerical methods to evolve them throughout the universe and predict observable signatures today, to be tested mainly via gamma-ray, radio and gravitational waves observations.

The project consists of understanding the evolution of primordial magnetic fields before Recombination. The phenomenology of magnetic fields decay is still debated, and to firmly establish the correct decay laws, numerical simulations are essential. In this context, we will mainly use the Pencil Code to simulate the magnetic field evolution from generation to Recombination, complemented with analytical understanding to interpret the simulation results.