Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
programming skills in Python Experience with machine learning systems or LLM-based architectures Experience working with complex data systems or developing applied AI prototypes Familiarity with modern AI tools
-
charge of your own research You will take part in the professorship’s academic and social activities You will teach one course of your choice per semester (2 weekly hours) in the MAGPW or SiP programs
-
to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
-
material characterization with a corresponding scientific publication record Strong communication, organizational, and analytical skills Proficiency in written and spoken English; German skills are a plus We
-
laboratory analysis, including generating isotopic or trace element data You have strong quantitative skills and comfort with coding, and good analytical thinking You are committed to working in
-
willing to exchange with your fellow team members, support them, and learn from them. Non-traditional research topics, questions and methods are encouraged We also encourage projects on non-European
-
quantitative skills and comfort with coding, and good analytical thinking You are committed to working in an interdisciplinary team blending climate modelling, geochemical process models, and new geochemical
-
related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
-
, analytical pipelines, and digital tools. Liaison between the Department of Chemistry, Research IT, and central university IT services. Contributing to the design, implementation, and maintenance of research IT
-
-quality research. Strong analytical skills, particularly with in-situ techniques (e.g., EPMA, LA-ICP-MS, SIMS). Experience in field work, geochronology (e.g., U-Pb, Ar-Ar) and/or numerical modelling