Plastics play a central role in our society due to their low production costs, high versatility and malleability as materials. Their desirable properties are achieved via specific combinations of the polymer matrix and various chemical additives (plasticizers, pigments, etc). However, these additives—of which many are hazardous to human and environmental health—can leach out of the polymer matrix and cause adverse effects. Moreover, the high complexity of additives used in plastics hampers effective re-use and recycling strategies and limits realistic pathways towards circular and sustainable plastics. In parallel to ongoing negotiations for a Global Plastic Pollution Treaty there have therefore been clear calls for simplifying the chemical fingerprint of plastics and phasing out toxic chemicals. In this context, utilizing the approach of Safe-and-Sustainable-by-Design (SSbD) for the development of alternative plastic materials is the logical way forward, as it places the assessment of safety and sustainability of the newly designed materials (together with its desired function) into the early stages of the design process. A key step here would be the identification of a subset of additives, which fulfill key functions—in a wide range of polymer types and plastic applications—while being safe with respect to human and environmental health.

Scientific challenges
There are several challenges related to the simplification of the plastic additive chemical space to enable the production SSbD plastics. These challenges are mainly due to the complexity of this space—10.000 chemicals have been identified as potentially used in plastic production—as well as the lack of experimental data on environmental fate and toxicity. Additionally, current models for in-silico prediction of chemical properties pertinent to hazard or environmental fate assessment are based on quantitative structure activity relationships (QSARs), which suffer from very limited applicability domain, sparse training sets, and too restrictive assumptions. This makes them error-prone and poorly applicable to a wide range of chemicals differing strongly from the training set. Additionally, these QSAR models tackle one toxicity/fate parameter at a time, which has been shown to be inadequate to assess safety and sustainability of chemicals (e.g. PFAS). Recent applications of machine learning combined with Bayesian network models have shown a great potential in providing a more accurate assessment of the fate and toxicity of chemicals. However, the current applications require extensive measurements both for fate/toxicity and environmental occurrence.

Objectives & approach
In this project an interdisciplinary team of (environmental) chemists and data scientists at the University of Amsterdam will tackle these challenges together with a network of external partners from academia, regulation and industry. We propose a data-driven approach towards simplifying the suite of chemical additives used in plastics to support the development of Safe-and-Sustainable-by-Design (SSbD) polymeric materials. We will develop advanced computational tools to make use of the 3D structure of chemical additives to score them according to specific SSbD criteria, while taking into account the polymer functionality. The final goal is to generate a short-list of safe chemical additives covering a range of key functions which can be used as to formulate simplified and harmonized plastic formulation across a wide range of application sectors. Finally, these models will be applied to the chemical space outside of the known plastic additives to identify potential novel SSbD plastic additives.

You enjoy learning about new molecular systems and AI algorithms. You are able to explain your work to both scientists with experimental as well as with theoretical and informatics background. You workindependently and can reflect on your own results and conclusions.

Your experience and profile

• PhD in science, preferably in domains of physics, chemistry, biology or computational science with a specialization in natural sciences / related to the project of your choice;
• Good programming skills (e.g. in C, C++ or Python);
• Experience with either machine learning or molecular dynamics simulations is required. High-calibre candidates with experience in both fronts are preferred;
• Motivated to closely collaborate with experimentalists, and possibly perform experiments.Experience with lab work or collaboration with experimentalist is preferred;
• Flexible, readiness to participate in interdisciplinary cooperation and multidisciplinary development(verifiably focused on collaboration with other disciplines);
• Committed researcher, demonstrated by publications in international refereed academic journals andacademic publishers;
• Strong initiative and good time management skills;
• Professional communication skills in oral and written English.

We offer a temporary employment contract for 38,00 hours per week for a period of 12 months. After a positive assessment this can be extended by 8 months. The preferred starting date is May 1, 2024.
The gross monthly salary, based on 38 hours per week and dependent on relevant experience, rangesbetween € 3,226.- to € 5,090.- (scale 10). This does not include 8% holiday allowance and 8,3% year-endallowance. The UFO profile Researcher 4 is applicable. A favourable tax agreement, the ‘30% ruling’,may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities is applicable.

Besides the salary and a vibrant and challenging environment at Science Park, we offer you multiplefringe benefits:

• 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January;
• Multiple courses to follow from our Teaching and Learning Centre;
• Multiple courses on topics such as leadership for academic staff;
• Multiple courses on topics such as time management, handling stress and an online learningplatform with 100+ different courses;
• 7 weeks birth leave (partner leave) with 100% salary;
• Partly paid parental leave;
• The possibility to set up a workplace at home;
• A pension at ABP for which UvA pays two third part of the contribution;
• The possibility to follow courses to learn Dutch.

Are you curious to read more about our extensive package of secondary employment benefits, take a look here.

Faculty of Science

The University of Amsterdam is the Netherlands’ largest university, offering the widest range of academicprogrammes. At the UvA, 30,000 students, 6,000 staff members and 3,000 PhD candidates study and workin a diverse range of fields, connected by a culture of curiosity.

The Faculty of Science has a student body of around 8,000, as well as 1,800 members of staff working ineducation, research or support services. Researchers and students at the Faculty of Science are fascinatedby every aspect of how the world works, be it elementary particles, the birth of the universe or thefunctioning of the brain.

The Van ‘t Hoff Institute for Molecular Sciences (HIMS) is one of eight institutes of the University ofAmsterdam (UvA) Faculty of Science. HIMS performs internationally recognized chemistry andmolecular research, curiosity driven as well as application driven. This is done in close cooperation withthe chemical, flavour & food, medical and high-tech industries.

Research is organized into four themes: Analytical Chemistry, Computational Chemistry, Synthesis & Catalysis and Molecular Photonics.

The Analytical Chemistry Group focuses on the development of new or greatly improved techniques or methods for the analysis of complex mixtures. The technical focus is on one- and two-dimensional separation methods, on hyphenation with detection and sample-preparation systems and on chemometric techniques for data handling and optimization. The application focus is on macromolecules, forensic science and a variety of other fields.

Want to know more about our organisation? Read more about working at the University of Amsterdam.