13 formal-method Postdoctoral positions at Chalmers University of Technology in Sweden
Sort by
Refine Your Search
-
. Project overview The postdoctoral researcher will develop and evaluate methods for recycling spent sodium-ion batteries. The work will involve experimental research in aqueous chemistry, with particular
-
Kahl (Computer Vision, Chalmers), Kathlén Kohn (Algebraic Geometry, KTH), and Mårten Björkman (Robotics, Perception and Learning, KTH). The research focuses on developing novel machine learning methods
-
(AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians working collaboratively. Our focus is on developing practical methods that blend traditional disciplines
-
magnets with functional, high-temperature magnetoelectric coupling. The theory position in the group of Sophie Weber will focus on (a) development of group-theoretical methods to rapidly identify and design
-
Chalmers University of Technology focused on the recycling of carbon fibre composites. The project aims to develop a novel method for recovering fibres using magnetic fields, with the goal of lowering
-
://www.chalmers.se/math/ At the division of Applied Mathematics and Statistics we conduct research within probability theory and its applications, the theory and implementation of finite element methods, inverse wave
-
methods, and magnetometry. Who we are looking for To qualify for the position of postdoc, you must hold a doctoral degree in Physics, Material Science, Aerosol Technology or another relevant field, awarded
-
control. Creating generalizable tools for various battery types, geometries, and chemistries. The scope of methods and applications will be tailored in collaboration with the selected candidate. The work
-
the models and measurement methods. The position is based at the Division of Transport, Energy and Environment within the Department of Mechanics and Maritime Sciences . The division conducts
-
and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical