Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- UNIVERSITY OF HELSINKI
- University of Oulu
- AALTO UNIVERSITY
- University of Turku
- ELLIS Institute Finland
- Itä-Suomen yliopisto
- Aalto University
- Helsinki Institute for Information Technology
- LUT University
- Natural Resources Institute Finland (Luke)
- Tampere University
- University of Jyväskylä
- 2 more »
- « less
-
Field
-
-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
-
machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
-
position within a Research Infrastructure? No Offer Description Research Assistant wanted! Join research on forest and peatland biodiversity and restoration. Work with remote sensing data, machine learning
-
(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
-
organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning and artificial intelligence methods, targeted validation
-
Helsinki, Vaasa, Porvoo, and Hamina. Helsinki, Pitäjänmäki: motors, generators, drives, robots, CPM energy management systems and paper machine drive solutions, global ABB Ability™ platform development, and
-
net period of time, which does not include parental leaves, military service etc.) good skills in spoken and written English motivation for research work in aerosol physics or chemistry. Please note
-
to promoting diversity, equality, and non-discrimination in all its activities. Thus, we promote equal opportunities to learn, acquire knowledge, participate, and make a difference. As an equal-opportunity
-
machine learning techniques, and GPU programming. The simulation results will be compared to observational data obtained using facilities worldwide including ESO and NOT. Who we are looking for A successful
-
resolution by integrating plasmonic nanopores with a high-speed Raman detection system, an automated control system, computer simulations, and advanced Raman-based bioinformatics. The RamanProSeq consortium