Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Umeå University
- SciLifeLab
- Linköping University
- Swedish University of Agricultural Sciences
- Uppsala universitet
- University of Lund
- Lulea University of Technology
- Luleå University of Technology
- Luleå tekniska universitet
- Lunds universitet
- Mälardalen University
- Blekinge Institute of Technology
- Chalmers University of Technology
- Göteborgs universitet
- Institute of Neuroscience and physiology, Sahlgrenska Academy, university of Gothenburg
- Institutionen för Biologi och miljövetenskap
- KTH Royal Institute of Technology
- Karolinska Institutet, doctoral positions
- Linnaeus University
- Sveriges Lantbruksuniversitet
- The Swedish University of Agricultural Sciences
- The Swedish University of Agricultural Sciences (SLU)
- Umeå universitet
- 13 more »
- « less
-
Field
-
robotics. Specific application areas of focus are long-term autonomous missions in large and uncertain environments, semantic mission planning with foundation models, agentic task decomposition and event
-
radiopharmaceuticals. Working with experimental tumor models, including cell culture, establishment of tumor models, and monitoring and care of laboratory animals. Performing biological sample collection and
-
and constantly changing planet. To understand how this planet works, our researchers seek answers in the field, through laboratory experiments and advanced models. Our research and education cover a
-
of software infrastructure to optimize data workflows Developing analytical methods to integrate different types of sequencing-based DNA data. Developing computational models for prognosis and treatment
-
to annotated cell types Experience of working with non-model organisms, where reference resources are incomplete and annotation cannot be taken for granted Strong programming skills (e.g. R and/or Python) and
-
robotics. Specific application areas of focus are long-term autonomous missions in large and uncertain environments, semantic mission planning with foundation models, agentic task decomposition and event
-
combine ultrafast pump–push–probe experiments with sub-10 fs resolution, finite-element simulations, and quantum models extending the Tavis–Cummings Hamiltonian. The aim is to demonstrate coherent control
-
of engineered underground hydrogen storage in lined rock caverns (LRCs) excavated in hard crystalline rock. Your tasks are to: - develop coupled thermo-hydro-mechanical numerical models to simulate hydrogen
-
complex contexts through statistical models, machine learning (ML) methods, and artificial intelligence (AI). This includes working with performance, scalability, resilience regarding platform architectures
-
. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification