POSTDOC/PHD POSITION: The University of Helsinki’s Department of Computer Science invites applications for a Postdoctoral or Doctoral Researcher position in Sample-Efficient Probabilistic Machine Learning. This opportunity, funded by the Research Council of Finland, offers a unique chance to join the Machine and Human Intelligence research group led by Principal Investigator Luigi Acerbi.
Designation: Postdoctoral or Doctoral Researcher
Research Area: Sample-Efficient Probabilistic Machine Learning
Location: Department of Computer Science, Faculty of Science, University of Helsinki, Finland
Eligibility/Qualification: The ideal candidate should have a strong background in computational statistics and/or machine learning, with experience in Bayesian methods. Proficiency in Python (e.g., PyTorch) and familiarity with Gaussian processes and/or Bayesian optimization are advantageous. For the postdoctoral position, previous research experience and publications are required. Applicants should hold or be about to obtain a Master’s degree (for the PhD position) or a PhD (for the postdoctoral position) in relevant fields.
Job Description:
POSTDOC/PHD POSITION IN SAMPLE-EFFICIENT PROBABILISTIC MACHINE LEARNING
The selected candidate will work on developing new machine learning methods for smart, robust, and sample-efficient probabilistic inference, with applications in scientific modeling, particularly in computational and cognitive neuroscience. The project involves conducting theoretical and applied research, designing and programming machine learning software, computational and data analyses, writing research articles, and participating in academic conferences.
How to Apply: Submit your application with attachments to Luigi Acerbi (luigi.acerbi@helsinki.fi), specifying “Application for PhD/postdoc in Sample-Efficient Probabilistic Machine Learning” as the email subject. The application should include a CV, transcripts, a cover letter, contact details of two referees, and an extended sample of scientific writing.
Last Date for Apply: Applications will be considered until the position is filled.
Table:
| Study Area | Sample-Efficient Probabilistic Machine Learning |
|---|---|
| Location | University of Helsinki, Finland |
| Eligibility/Qualification | Strong background in computational statistics and/or machine learning. Proficiency in Python and familiarity with Bayesian methods are advantageous. |
| Job Description | Develop sample-efficient probabilistic machine learning methods for scientific modeling in computational and cognitive neuroscience. Conduct research, design and program software, analyze data, and contribute to academic conferences. |
| How to Apply | Submit application to Luigi Acerbi (luigi.acerbi@helsinki.fi) with specified subject. Include CV, transcripts, cover letter, contact details of referees, and an extended sample of scientific writing. |
| Last Date for Apply | Until the position is filled. |
