Sort by
Refine Your Search
-
the interface of education and social work and ambition to advance one of the aforementioned subject areas Training and experience in qualitative empirical methods/mixed methods research Openness to further
-
potential of genetic discovery. We have access to extensive biosample collections and use advanced omics technologies and genetic-epidemiological methods on human study populations to identify molecular
-
track record on one of the following research areas: Trustworthy AI AI for formal methods Formal methods for AI The successful candidate will participate in the activities of the research group led by
-
extension. Job description We are looking for applicants with interests in the biogenesis and turnover of nuclear RNA and its relationship to gene expression regulation in mammalian cells. Applied methods
-
an innovative multi-method design, the project integrates: Daily diary and ecological momentary assessment (EMA) approaches In-depth qualitative and immersive fieldwork conducted by the geography team A key
-
of the methods. The project is carried out in close collaboration with Helical-AI, an industrial partner specialized in large-scale genomic foundation models and HPC-enabled model deployment, ensuring
-
nanoparticles for gas-phase catalysis. Development and application of ultrasensitive methods to probe gas–surface interactions by means of electron spectroscopy in the electron microscope. Operando studies
-
relevant to this area of research e.g. computer science, applied mathematics, operations research Strong expertise in exact and/or approximated methods, meta-heuristics and/or machine learning, Proven
-
immunogenicity. Over the past decade, this process has been well characterized, and robust methods have been developed to predict it with high confidence. In contrast, our understanding of the principles governing
-
, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks