Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
dimensionality reduction as well as analysis of blood proteomic data. We are looking for a student with experience in bioinformatics and relevant statistical methods using R or Python languages. Some knowledge of
-
control design, event-based systems, active sensing, neuromorphic engineering, vision for robotics. ● Research experience (e.g. through Master thesis work or research internships) in control theory or event
-
. Previous experience in mammalian cell culture, structural biology (cryo-EM) or membrane protein purification will be preferred. Candidates should demonstrate a strong motivation and commitment to solve
-
Bachelor and Master studies. A comprehensive resume including your full list of publications and project experiences, a research statement outlining your main research interest, evidence of language
-
. Experimentally characterize leakage inductance and power losses in conventional magnetics using various core materials. 2. Investigate the impact of harmonic content introduced by WBG devices on magnetic
-
/Electrical Engineering, or a related discipline. Experience with programming languages (preferably Python, C/C++). Strong analytical and problem-solving skills; motivated to conduct high-quality research with
-
health science field. A strong interest in allergy, immunology, military medicine, or clinical diagnostics. Experience with clinical research, statistics, or patient-centered data collection is a plus. The
-
on the research. Required Qualifications Candidates have a Master of Science in Computer Science, Computer Engineering, or equivalent. Experience in any of the following are highly relevant: software development
-
. Strong interest in immunology and neurodegeneration; experience with human samples and/or immunophenotyping tools is a plus (eg flow cytometry) Comfortable working at the clinic–lab interface; excellent
-
dynamics, control engineering, or dynamical systems. Required Skills/Qualifications: Experience with experimental methods (laboratory setups, sensors, data acquisition). Background in modeling of dynamical