Doctoral student in mathematical statistics focused on foundations of scientific machine learning - Hiring in process/Finished, not possible to apply

Subject description

Mathematical statistics includes probability and statistical theory with applications in all areas of society with an emphasis on science, technology, medicine and economics. The main task of probability theory is to develop mathematical models for the description and analysis of random events, and to study the mathematical properties of such models. Statistical theory includes the principles and methods for building and testing the models using empirical facts and data.

Project description

The aim of this project is to develop theory, methods, and software, for modelling and inference within the framework of scientific machine learning. Particular attention will be given to stochastic dynamical systems. The work will draw on topics such as Bayesian estimation, stochastic differential and difference equations, Gaussian processes, variational methods, and Monte Carlo methods. While the aim is to develop powerful statistical tools for learning and predicting in stochastic systems, attention is also given to algorithmic development for tractable inference.  

Work duties

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties could also include teaching and other departmental duties (no more than 20%). 

Admission requirements 

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant: 

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 ETCS credits of which at least 60 ETCS credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Mathematical Statistics if the applicant has: 

• at least 90 credits of relevance to the subject, of which at least 60 credits from the second cycle and a specialized project of at least 30 second-cycle credits in the subject, or
• a second-cycle degree in a relevant subject.

Finally, the student must be judged to have the potential to complete the doctoral programme.

Additional requirements

• at least one course in Programming
• at least one 2nd cycle course in Stochastic processes, Machine Learning, or related subjects such as: Time-series analysis, Spatial statistics, Probabilistic machine learning, Bayesian state estimation, Markov Chain Monte Carlo methods, Financial statistics.  
• Very good oral and written proficiency in English.

Assessment criteria

Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily based on academic results from the first and second cycle. Special attention is paid to the following: 

1. Knowledge and skills relevant to the thesis project and the subject of study.
2. An assessment of ability to work independently and to formulate and tackle research problems.
3. Written and oral communication skills.
4. Other experience relevant to the third-cycle studies, e.g., professional experience. 

Other assessment criteria:

• ability (shown via, e.g., a thesis project) to develop, implement, and apply relevant scientific machine learning models to data and critically assessing the results.
• experience in Bayesian estimation, stochastic differential equations, Gaussian processes, optimization, probabilistic machine learning. 
• programming experience (preferably in Julia, Python, Matlab, R, or similar)

Consideration will also be given to good collaborative skills, drive, and independence, and how the applicant, through experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme. Read more on the University website about being a Lund University employee Work at Lund University. 

Terms of employment 

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (when including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

How to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.). 

Welcome to apply!