Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
«Detaljer» for å lese mer om dette og tilpasse dine valg. Om cookies Bare nødvendige cookies UiO/Anders Lien 16th August 2026 Languages English English English PhD Research Fellow in Mars Atmospheric
-
Vienna Location Wien, Österreich Salary Competitive Closing date 1 Feb 2027 View more categories View less categories Discipline Applied Science Job Type PhD Position Employment - Hours Full time
-
is an emerging research field that uses machine learning, data analysis and computational modelling to guide the discovery, characterization and optimization of new materials. By accelerating
-
research, realistic-scale experiments, exploratory laboratory-scale studies, data management and modelling. The institute has modern research facilities and is ISO 9001 certified. Your area of work will
-
Experience, Skills & Qualifications: PhD level research experience in environmental sciences, with strengths in analytical or computational methods (e.g., remote sensing, spatial analysis, systems or
-
research infrastructure, including human stem cell and organoid platforms, genome engineering technologies, advanced microscopy, spectral flow cytometry, single-cell and spatial genomics, proteomics and
-
University of British Columbia - Department of Medicine | Northern British Columbia (Fort Nelson), British Columbia | Canada | about 1 month ago
–75,000 per annum plus benefits. Closing date 7 Nov 2026 Website https://ubc.wd10.myworkdayjobs.com/ubcfacultyjobs/job/UBC-Vancouver-Campus---Vancouver-BC-Canada/Postdoctoral-Research-Fellow---Human
-
, autonomous systems must handle strict spatial and temporal constraints while adapting in real time to supervisory commands, unexpected obstacles, and environmental disturbances. Enabling trustworthy human
-
learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable strategies for estimating archaeological potential, that capture distinct criteria
-
archaeological data layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient