Two open PhD-positions
The Institute for Atmospheric and Earth System Research, INAR, University of Helsinki, and the Department of Computational Science, Lappeenranta University of Technology, have two open Ph.D. positions. The successful applicants will work in the Atmosphere Modelling Centre (AMC) in Lahti, strongly connected to the Multi-Scale Modelling (MSM) group at INAR.

Our group aims to apply novel machine learning and artificial intelligence techniques to different atmospheric topics. We will also further develop and use our new FLEXPART/SOSAA modeling system to investigate the formation and growth of secondary organic aerosols in urban and rural areas and their impact on climate and air quality predictions. A short description of the two open PhD-positions is at the end of this email.

The new AMC Lahti has scientists from both fields (atmospheric and computational science), and strong collaboration within the group will have a crucial impact on reaching a fundamental understanding in combining both fields. Applicants should have an education in atmospheric science (focus on atmospheric or particle-phase chemistry) or computational science.

If you are interested, please send your CV, list of publications, a short motivation letter, and two references latest by the 31st of May 2023 to The position can start on the 1st of July 2023 or later by agreement.

Position 1: Air pollution with high concentrations of ultrafine particles is one of the top five global health risks and causes more than six million premature deaths yearly. Understanding the atmospheric autoxidation reactions of the biogenic and anthropogenic emitted volatile organic compounds is imperative as the species formed by autoxidation via their strong potential to produce SOA and reactive substances impact health. However, our knowledge in this area of chemistry and the impact of the reaction products on the formation of secondary organic aerosols (SOA), which represents an essential fraction of the particulate matter, is limited. This project grows from the need to understand better gas-phase oxidation reactions that produce SOA.

Today, the bottleneck in creating the chemistry schemes for SOA formation has been an expert working time since one experienced scientist needs several months to make a single autoxidation chemistry scheme fit experimental data. Moreover, such a fit lacks any statistical analysis of the uniqueness and optimality of the outcome. This project obliterates the bottleneck of expert working time by using computational mathematics instead of manual work. The project allows us to investigate more complex chemical systems under all relevant conditions, significantly increasing our understanding of SOA formation and statistical quantification of remaining uncertainties. Achieving this will be a breakthrough in atmospheric sciences.

The Ph.D. will be officially at the Lappeenranta University of Technology, and the primary supervisors of this project are Professors Michael Boy and Heikki Haario. Additionally, Professor Gordon McFiggans from the Department of Earth and Environmental Sciences at the University of Manchester, United Kingdom, and Dr. Lukas Pichelstorfer, self-employed scientist (Salzburg, Austria) will co-supervise this thesis. The position will involve frequent visits to Manchester and Salzburg.

Position 2: Gas-phase oxidation reactions and particle-phase chemistry strongly impact atmospheric air quality and climate change. Despite its importance, SOA formation still needs to be better understood on the molecular level, and especially the ongoing oxidation chemistry inside particles is far from well-known. Successfully reducing its adverse effects on society and the environment requires a mechanistic understanding of the underlying processes.

This project will establish a new particle-phase chemistry module by applying machine learning techniques (neural network) on simulated condensed compounds and the measured particle phase compositions. The Ph.D. candidate will also investigate how the new particle-phase oxidation schemes and the resulting SOA formation will increase our knowledge of air quality at several selected stations in urban and rural areas. The candidate will apply our newly developed FLEXPART-SOSAA model system, which is intensively used and further developed in the MSM/AMC group.

The Ph.D. will be officially at the University of Helsinki, and the principal supervisor of this project is Professor Michael Boy and Assistant Professor Andreas Rupp. Additionally, we will appoint one international co-supervisor at the beginning of the project.