Bulk properties are influenced by particle attributes, such as particle size, shape, and chemistry, defined during crystallization and/or milling processes. Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to developing robust production routes. This places strict demands on bulk material flow properties such as blend uniformity, compactability, and lubrication, which need to be satisfied. Consequently, making the flow prediction of pharmaceutical materials during early-stage development is increasingly important. Currently, the suitability of raw materials and/or formulated blends for product development requires detailed, time-consuming experimental characterisation of the bulk properties. The project aims to demonstrate the applicability of predictive models towards product manufacturing using particle informatics and to improve explainability/model confidence in the results. The project will use the novel coupling of experimental characterisation and computed molecular and particle features to achieve this. This will culminate in a framework that allows for an explainable and interpretable machine learning model. This project will deliver a novel, interpretable machine-learning model for predicting powder flow considering physical, chemical, and computed molecule/particle features. The output of this model will facilitate the rapid development of new medicines manufacturing.

The PhD project aims to accelerate the manufacture and deployment of cost-effective mRNA vaccines by optimizing production processes and improving scalability. This research will involve developing novel lipid nanoparticle synthesis methods, including particle characterization purification and formulation to enhance vaccine stability and efficacy. Additionally, the project will explore innovative delivery systems to ensure efficient and targeted immune responses. By addressing current limitations in mRNA vaccine production and distribution, this work seeks to contribute significantly to global health initiatives, particularly in response to pandemics and infectious diseases.

Antibody Drug Conjugates (ADCs) are an emerging class of chemologics which have wide ranging applications from target validation to new therapeutics. Currently, only a limited palette of synthetic approaches have been employed to attach the drug payload to the parent mAb. The proposed study aims to exploit novel reactivity and selectivity patterns identified in our laboratories using a key reactive intermediate generated photochemically in order to create a modular, convergent synthesis of ADC with tuneable linker properties. The approach will be exemplified using a workhorse mAb in conjunction with a variety of linker-payload combinations to enable convergent synthesis of ADC systems. Potential additional applications of this approach also exist in screening of fragments - small efficient ligands which can target a protein of relevance to disease. Equipping a library of such fragments with the reactive warhead of the type outlined above enables these to covalently modify a protein. This binding event may be detected by mass spectrometry and the hits obtained validated by biochemical assays before being evolved into mature leads.

We propose to use individual-level data from the Ministry of Justice (MoJ) on the universe of offenders in Britain (several million observations) to assess the effect of disclosure requirements for criminal records on recidivism and crime. The project leverages a natural experiment created by the 2014 amendment to the 1974 Rehabilitation of Offenders Act (ROA) which reduced the period during which prior offenses had to be disclosed by ex-convicts to potential employers. The reform created an exogeneous "rehabilitation shock" by removing disclosure requirements for some offenders but not for other very similar offenders. We combine rich data with cutting-edge econometrics - including a regression discontinuity design, the differences-in-differences method, and descriptive approaches - to isolate the causal effect of disclosure requirements on recidivism. The data contain detailed and longitudinal information on individual offenders' sentences, release dates, and socio-economic background, allowing us to conduct important subgroup analyses. The PhD student will be supervised by experienced supervisors who are experts in quantitative data analysis. We offer an excellent research environment that features collaboration with experienced researchers, access to state-of-the art safe data facilities, and training in cutting-edge statistical and econometric methods. The results of the PhD research will be timely and original, not least because to date the 2014 ROA reform has not been evaluated using a large and nationally representative dataset and quantitative methods. There is a large literature gap with respect to research on the causal effect of disclosure requirements on recidivism. By way of our strong knowledge exchange network in the form of the Fraser of Allander Institute, the PhD will be able to disseminate their results to policy makers and other stakeholders, thus ensuring that this research has impact.

Ultracold atoms in optical lattices offer unique possibilities as quantum simulators for the study of many-body quantum systems, relevant for example to material science and other disciplines. To further enhance the capabilities of these systems, we use programmable static and dynamically varying light potentials - a technology that we developed in WP6 within the first years of the QCS Hub programme. Our approach uses a spatial light modulator to create arbitrary potentials which we can project onto the atoms with sub-wavelength resolution by our quantum-gas microscope. The same microscope setup enables us to detect the atoms in a two-dimensional optical lattice with single-lattice-site resolution. In the first part of this PhD project, we will use programmable light potentials to create a so-called Lieb lattice, by 'blocking' the central site in each 3x3 sub-cell of a square lattice with a repulsive potential (Fig. 2). Lieb lattices exhibit very interesting properties such as flat bands, localization and edge states. Based on model calculations which we have already done in collaboration with the local theory team in WP6, we would first use the site-resolved detection to confirm the existence of the of edge states, and study their dynamical evolution, and later do so as well in more complex lattice geometries. We will prepare initial states using another tailor-made light field that light-shifts selected atoms such that they can be transferred to a different hyperfine state with a microwave field, before all other atoms are removed. In a second project strand, we will use the tailor-made light-potentials to produce microscopic reservoirs with, e.g., varying chemical potential, atom numbers, and strength of the coupling link between them. We will focus on the observation thermodynamics properties in the quantum regime and investigate out-of-equilibrium dynamics and transport between the reservoirs, with the additional option to tune the onsite-interaction via a Feshbach resonance.