65 machine-learning-"https:" "https:" "https:" "RAEGE Az" Postdoctoral research jobs in Finland
Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
will design and implement novel computer vision and machine learning methods for “sensorized” cameras that extract medically relevant features without transmitting raw video. You will evaluate algorithms
-
must have completed their doctoral degree less than five years ago at the time of starting in the position (a net period of time, which does not include parental leaves, military service etc.). good
-
7 Nov 2025 Job Information Organisation/Company Tampere University Research Field Computer science » Programming Computer science » Other Engineering » Computer engineering Engineering » Electrical
-
opportunities for professional development (https://www.helsinki.fi/en/about-us/careers ). Application should include the following documents as a single pdf file: a cover letter, a CV, a publication list the
-
occupational health care and health insurance, unemployment and pension fund, a generous holiday package, sports facilities, and opportunities for professional development (https://www.helsinki.fi/en/about-us
-
. The concept has lately gained increasing interest from researchers in applied mathematics and machine learning. This is due to its remarkable flexibility, mathematical elegance, and as it has produced state
-
Centre of Excellence (CoE) in Neutron-Star Physics (https://neutronstars.fi/ ), funded by the Research Council of Finland. The CoE status provides us with long-term funding, strong connections to related
-
. The initial salary is €4064, and the contract includes occupational health care. Our vast array of professional development opportunities means you will grow and learn, having the chance to participate actively
-
physics and probability. Successful candidate will work with Prof. Antti Kupiainen on problems of interest to the Simons Collaboration on Probabilistic Paths to Quantum Field Theory https://probabilistic
-
, including how DNAme potentially drives trait variation and how it responds to the environment. We will use machine learning tools to perform high-throughput phenotyping of birch leaves – specifically stomatal