Jobs

Continuous call for doctoral students and postdoctoral researchers

Machine learning and AI are extensively used in the sciences. When modelling physical systems, the understandability and statistical robustness of the models is often more important than predictive accuracy. We are looking for talented postdoctoral researchers and doctoral students to study explainable and understandable AI and the uncertainty quantification of AI models. While our AI methods are generic and not tied to any specific application domain, we work closely with scientists to build Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA).

Please see the VILMA job opportunities page for more generic non-AI VILMA jobs.

What we do and who we hire

Before contacting me about employment opportunities, please read the text below!

What do we do? - the big picture

While our main "application area" is often motivated by atmospheric processes described below, our machine learning / artificial intelligence (ML/AI) research is generic and published in quality ML/AI journals and conferences. The fundamental ML/AI topics covered are probabilistic emulator / predictive regression models for atmospheric processes, randomisation methods for interactive visual data exploration, advanced statistical methods for ML/AI, and explainable AI.

We work in a multidisciplinary team of computer and atmospheric scientists. We are setting up our new Virtual Laboratory for Molecular Level Atmospheric Transformations (VILMA) Centre of Excellence. VILMA aims to efficiently model atmospheric molecular level processes and understand the underlying mechanisms and causal connections. VILMA will combine first-principles quantum chemical and other simulations and probabilistic ML/AI models with interactive visualisation and exploratory data analysis.

Who can we hire?

Most of our work is "AI/ML theory" that is not specific to atmospheric topics but often can be applied there. The persons we hire should advance the AI/ML research agenda described above.

Any candidate should have essential ML and related mathematics knowledge and some programming skills. We will consider applicants with computer science, mathematics, atmospheric science, physics, and chemistry backgrounds.

Interest in the atmospheric science topics described above is considered an advantage but not an absolute requirement. Prior knowledge of atmospheric processes is optional. An interest in natural sciences is an advantage.

The contract duration for doctoral students and postdoctoral researchers is agreed upon individually; the entire course of PhD studies is typically four years, and postdoctoral contracts are typically between 1 and 4 years. We welcome PhD students and postdoctoral researchers both from Finland and abroad. Doctoral students should have completed an MSc degree before starting employment, and postdoctoral researchers should have a PhD.

Interns and MSc students - who have, e.g., done well in my classes - will work on research-grade problems that align with our scientific interests. We primarily seek students tentatively interested in continuing PhD studies in my group after the MSc degree; most of our PhD students have started as interns and MSc students. The contract will be agreed upon individually. Depending on the phase of studies, typical options are (i) a 3-month full-time internship project (often, but not necessarily, as a summer internship) or a part-time internship (which makes it possible to do some courses at the same time, a typical arrangement for students during teaching periods) that by mutual agreement can later continue to MSc or PhD thesis; or (ii) 6 month MSc thesis project that by mutual agreement can later continue to PhD thesis. Intern and MSc student applicants should have study rights in a Finnish university. We encourage interested students from outside Finland to apply to the relevant Bachelor's Programme or Master's Programme (e.g., data science, theoretical and computational methods, or atmospheric sciences) at the University of Helsinki.

All positions include salary with social security and pension fund payments according to Finnish legislation, Finnish national health insurance system access (details), and occupational health care services, among other staff benefits. The university covers work-related travel expenses. You can read more about practicalities from our website for arriving international staff and why you should choose the University of Helsinki.

PhD study supervision without hiring

As a main rule, I only supervise PhD students hired by a collaborating research group, organisation (e.g., a company or a research institute), or me. So, if you want to do doctoral studies with me, apply for a paid doctoral student position (as described on this page) or discuss it with my collaborator (i.e., your employer, who can then contact me). After hiring, you can apply for the applicable doctoral programme as your first job task.

How to contact us?

If you are interested in working with us, please email Kai Puolamäki a brief motivation letter (typically max. 1 page) where you explain three things: (i) why you would like to work with us, (ii) your primary interest in ML/AI, and (iii) why you are leaving your current position (e.g., graduation, want to change research topic, etc.) and what are your plans after the applied employment (e.g., MSc or PhD with us after an internship, academic or industry career after a postdoc period, etc.). Please attach a copy of your CV to your email. If you are interested in a student position, please also attach a study transcript that displays the grades in Finnish, Swedish, or English and clearly explains the grading scale, minimum passing grades, and the maximums; an unofficial copy is okay. If you are interested in a postdoctoral researcher position, please attach a list of publications.

I will consider the following three items when looking at your email: (i) skills and experience (demonstrated, e.g., by course grades for a student candidate or publications for a postdoctoral researcher candidate), (ii) topical fit (your skills, experience, and interests are relevant for our research plan), and (iii) motivation to work with us and interest in our ML/AI research topics (demonstrated, e.g., by your motivation letter and your earlier studies and work). Other documents (such as names of references, portfolio of your past work, degree certificates, etc.) are not typically necessary for the first contact. Please do not send copies of your publications or thesis. We'll contact you if we have an offering that fits your interests.

Examples of topics: ML/AI with real-world data

I've included some potential topics for summer work for 2024 below. On mutual agreement, any topic could be continued for the Master’s thesis. The themes (in bold font below) are suitable topics for a doctoral dissertation, with the summer work descriptions below serving as starting points. The topics can be tailored to suit the applicant's interests. 

1. Uncertainty quantification. We are looking for a summer intern interested in uncertainty quantification for machine learning. The student should have a good math/statistics background to develop on top of our recent uncertainty quantification framework based on Gaussian processes. More specifically, we are interested in estimating the rate of change of a function (Lipschitz constant), which can be used to derive an uncertainty estimate (estimated prediction error) for a supervised learning model. The student should also be familiar with Python or R to perform numerical experiments and extend our existing framework. Other references: Oikarinen et al. (2021), Savvides et al. (2023).
2. Robust, explainable artificial intelligence (XAI). We seek a summer intern interested in explaining and interpreting complex machine-learning models. The applicant should have a solid understanding of statistics and a grasp of the basic concepts of machine learning. Proficiency in Python/R is preferred. Our group is developing methods to explain predictions from various “black box” models in the context of natural sciences (see, e.g., our recent preprint on SLISEMAP). However, many widely used explanation methods produce unstable explanations. The student will experiment on, compare, and enhance the robustness of various explainable AI methods, especially concerning their applicability to natural science research. Other references: Björklund et al. (2022), Björklund et al. (2023).
3. Open-source software for science. We are looking for a summer worker interested in open-source software development, either to create de facto implementations for some of our previous research projects, such as BSV or to extend our existing software, such as adding more machine learning to our interactive data-visualization platform χiplot for the virtual laboratory of the VILMA Centre of Excellence. Additionally, we are interested in different ways of incorporating generative AI and other AI assistance into our workflows. We mainly use Python and R (including libraries such as PyTorch and JAX for high-performance computing) but also value web browser-based systems and interfaces such as Plotly and web-assembly to ease the collaboration with (non-CS) scientists (see χiplot for an example).

Here is a short link to this page: https://bit.ly/edahelsinkijobs.