2023 CMP Industrial Projects

Whilst the infrastructure and personnel described above aims to deliver the best research/outputs possible, all of the data collected is based on a daily, visual inspection of each sample handled by APEX/bouquet assessed by the MM Flowers QA team. Considering an APEX example, a typical sample would be a bunch of flowers, in which the assessor must consider the flowers, leaves and stems in their inspection, and use pre-determined criteria to describe the appearance of the sample on that given day. This criterion describes both individual flower stems that have 'failed' (e.g. due to the presence of disease), but also the general, aesthetic appearance of the flowers (e.g. colour change). APEX handles approximately 40-50k samples annually, with around 30-60 data points collected per sample, with the full dataset of what each assessor recorded available. Due to the scale of the samples handled, this requires a team of individuals who are responsible for assessing the flowers on a daily basis. Training is provided to all individuals with supplementary guides available, but the nature of visual inspections will always have a degree of subjectivity - this is particularly the case across 100s of species and varieties, and 1000s of samples assessed. A very similar approach is used by the MM Flowers QA team, albeit with more focus on ensuring each bouquet meets the specification(s) agreed with their customers.

Given the above problem, this therefore presents a number of questions to address:
1. Using historic data, is it possible to quantify the degree of variability between individuals when assessing samples of cut flowers/commercial finished bouquets?
2. If there is clear variability, is this focused on certain aspects of the flowers or criterion available, or are there biases towards flower types, treatments etc?
3. Can recommendations be made around the methodology used by APEX/the MM Flowers QA team to try and minimise the subjective effect?

Integrating high content biological profiling techniques has the potential to enable drug discovery teams to characterise compounds based on their multivariate biological profile (phenotypic response), as opposed to just the chemical structure and univariate data. This would enhance our ability to find new medicines for patients by providing a broader vision to the biochemical response the drug elicits in a patient.

We have generated and processed multimodal data (metabolomics, proteomics, transcriptomics and imaging) for compounds against multiple protein targets to address important questions for characterising drug-candidates, such as identifying undesirable biological effects caused by these compounds. Current literature, computational techniques and best practices are limited for interfacing with this type and scale of data and opportunities exist for novel research, exploration and even publication.

We would like a student to join us over the summer to:
1. Investigate our existing data workflows and explore new methods of statistical analyses on large biological datasets, eventually formulating a decision tree for appropriate use of our suite of tools.
2. Benefit from our extensive readily available datasets to explore various applications of machine learning to high content profiling data (please refer to the GitHub repository on Phenonaut for a reflective example).
3. Contribute to our existing codebase for analysing and AI modelling biological data, helping drive business decisions on critical stages of the drug discovery pipeline (such as hit qualification).

Silvaco is the software engineering company developing the tools to assist in manufacturing of semiconductor devices. In UK office we mostly work on 'process simulation' side -- mathematical modelling of the processes used in manufacturing.

One of such processes is implantation -- bombardment of piece of (typically) Si with ions (dopants), to change the electrical properties of the target in specific areas. To predict the final ion distribution we use Monte Carlo simulation -- follow the path of large number of ions, as they fly through the structure. There are several effects involved, one of them -- ions 'bouncing' off the crystal grid atoms. We are currently reviewing the legacy code and are interested in mathematical representation of regular crystal grid. That is, for arbitrary point in space we need to know the distance to and position of the closest atom of the grid.

Your task would be to review the literature and to suggest and to implement the required algorithm for Si and/or for SiC crystal grid.

Iconal is a technology consultancy based in Cambridge, specialising in new and emerging technologies for homeland security applications. This will be our fifth year offering CMP placements, and we are looking for a summer student to join our small team and get involved in our research and development activities.

Our ideal candidate is self-motivated, innovative and enthusiastic about the practical application of maths to solve real-world problems. We are offering four projects this year and students should indicate their project(s) of choice in their application. Unfortunately, due to the nature of our work, we are only able to consider applications from UK nationals or individuals with long-standing residency in the UK.

We will accept applications until 1st March and organise interviews shortly after this date.

Iconal is a technology consultancy based in Cambridge, specialising in new and emerging technologies for homeland security applications. This will be our fifth year offering CMP placements, and we are looking for a summer student to join our small team and get involved in our research and development activities.

Our ideal candidate is self-motivated, innovative and enthusiastic about the practical application of maths to solve real-world problems. We are offering four projects this year and students should indicate their project(s) of choice in their application. Unfortunately, due to the nature of our work, we are only able to consider applications from UK nationals or individuals with long-standing residency in the UK.

We will accept applications until 1st March and organise interviews shortly after this date.

Iconal is a technology consultancy based in Cambridge, specialising in new and emerging technologies for homeland security applications. This will be our fifth year offering CMP placements, and we are looking for a summer student to join our small team and get involved in our research and development activities.

Our ideal candidate is self-motivated, innovative and enthusiastic about the practical application of maths to solve real-world problems. We are offering four projects this year and students should indicate their project(s) of choice in their application. Unfortunately, due to the nature of our work, we are only able to consider applications from UK nationals or individuals with long-standing residency in the UK.

We will accept applications until 1st March and organise interviews shortly after this date.

Iconal is a technology consultancy based in Cambridge, specialising in new and emerging technologies for homeland security applications. This will be our fifth year offering CMP placements, and we are looking for a summer student to join our small team and get involved in our research and development activities.

Our ideal candidate is self-motivated, innovative and enthusiastic about the practical application of maths to solve real-world problems. We are offering four projects this year and students should indicate their project(s) of choice in their application. Unfortunately, due to the nature of our work, we are only able to consider applications from UK nationals or individuals with long-standing residency in the UK.

We will accept applications until 1st March and organise interviews shortly after this date.

To decarbonise the world’s power generation, the growth of Renewable Energy projects is expected to further accelerate across Europe and globally. Historically, wind and solar projects largely received stabilised revenues through government support, but in recent years they increasingly rely on receiving floating power prices or direct offtake from corporates and utilities via fixed price power purchase agreements. With multi-decade investment horizons, this introduces the risk of either volatile power risk or long term counterpart risk as key uncertainties for renewable energy investors.

As a dedicated climate focused credit risk insurer, we are considering some new products that may help manage these risks for renewable investors.

We are curious to understand whether historical prices in power markets can be explained mathematically, and whether that can be used to determine more and less likely future price scenarios. We’re also curious if that is the same for all power markets, or whether they statistically behave differently market (country) by market or if they follow similar distributions. So the first part is a research and data science project around power prices. The second part, is that we use credit quality models to determine probabilities of credit default for counterparts, derived from credit rating agencies, that may be improved upon. We would like to understand if there is available data and analysis on corporate payment behaviour around electricity bills, or whether our current approach is sound.

The ultimate request is around combining counterpart payment behaviour with scenarios on likely future electricity prices. This allows construction of a model/ tool to run future scenarios on both, and allow us to take a view on financial risks involved and whether this then translates into an insurable risk.

In the world of pathology, the gold standard for diagnosing many pathological conditions is the procurement and analysis of a biopsy. A biopsy is a small sample of tissue from the patient, which is embedded in paraffin wax to make a tissue block. 3 − 4μm thick slices are then laid onto a transparent glass slide, and stained to make certain features easier to observe. These biopsies are analysed by pathologists in order to make a diagnosis.

Modern machine learning is currently revolutionizing the analysis of biopsies; machine learning models have been trained on whole slide images (WSI) of biopsies that have been annotated, by pathologists and scanned using a digital slide scanner at 0.25 − 0.50μm/px to perform many complex image processing tasks, such as locating diagnostically relevant tissue [2], segmenting and classifying different cell nuclei as distinct objects [3], and making diagnosis predictions [5], even with minimal, weak annotations [4].

One of the many challenges with training machine learning algorithms on WSI data is generalizability. For example, pathologists sometimes add pen markings, labels, and other spurious features on the slides of the biopsies as reminders of important clinical data or diagnostic observations. This can confound the training of the neural network; if a neural network can identify that red pen is often used to signify a specific diagnostic feature, then the neural network will learn that red pen is indicative of that diagnosis instead of analysing the actual tissue.

In this project, a separate algorithm will be developed that can identify these spurious features and either remove or replace them. This would remove bias and data leakage from the dataset, thus improving performance and generalizability of the neural network. This has the potential to remove one of the big barriers to rapid and accurate WSI classification, improving accuracy and generalizability of a diagnostic tool in a real-world setting.

The project would entail the following steps:

1. Use QuPath (an open-access software platform for bioimage analysis) [1] to manually segment spurious features from WSIs carefully selected by Ben Schreiber (a 4th year PhD student working on digital pathology). These WSIs can be used as training and/or validation data.

2. Write a program that randomly draws convincing spurious features on WSIs. This can be used to artificially increase the number of WSIs containing spurious features.

3. Manually add easily removable spurious features (e.g. pen marks) to the physical biopsy slides themselves and have them scanned.

4. Write an algorithm to either:
(a) Flag WSIs which contain spurious features
(b) Segment spurious features in WSIs and replace them with background

The algorithm can be based on traditional image-processing techniques (Gabor filters, Canny edge detection, entropy filters, etc.), on machine learning approaches (neural networks, random forest, K-means clustering, etc.), or a combination of the two. The student should feel free to select a method that they feel best suits the task at hand.

In a fast-moving consumer goods environment, it’s vital that safety assessments are conducted to ensure products are safe for humans and the environment either during the production or the use of products. These risk assessments historically have heavily relied on the use of in vivo animal testing to identify detrimental impacts of chemicals on organisms. which are neither ethical or efficient facing sustained societal pressure to remove the use of animals testing for safety purposes.

For more than 20 years, Unilever has been working towards developing Next Generation Risk Assessments (NGRAs) leveraging on advances in biology, genetics, computational sciences, mathematics and statistics to develop novel in silico and in vitro based methods, vulgo New Approach Methodologies (NAMs) which can be used to robustly support safety decision without testing on animals. [1, 2]

The current evolution in the risk assessment paradigm, moving away from in vivo tests towards mechanistic based information to support decision-making, creates new opportunities for the use of NAMS data as hazard descriptors. However, this also creates an information mismatch since Environmental Risk Assessment is anchored on the comparison between a Predicted No Effect Concentration (PNEC) which represents the maximum water concentration at which no hazardous effect is anticipated, with exposure, as defined by the Predicted Environmental Concentration (PEC). However, in vitro data reflect an effect concentration at the target site (i.e. internal concentration) to the organism. To re-align the two, it is fundamental to have the ability to convert the in vitro generated hazard Point of Departure (PoD) to the same level of the PEC i.e., from concentration at target site towards external concentration required to achieve PoD concentration internally, making the availability of robust quantitative in vitro to in vivo (qIVIVE) /kinetic mass-transfer models fundamental.

One solution to this problem makes use of advanced toxicokinetic models, anchored on chemical characteristics and biological processes to parameterize the inflow and outflow of chemicals between the organism and the water surrounding it. These are able to relate the external concentration to the concentration at the target site and vice versa. [3]

This project will fundamentally explore the use of advance mathematical methods to solve complex biologically relevant problems, with an immediate impact on chemical safety decision processes, in a multi-disciplinary way. Several toxicokinetic models exist for a variety of organisms, with their respective predictive performance being an area of open debate. Especially for the purpose of safety assessment, the measure of uncertainty is vital, therefore there is a need to evaluate the models’ performance against existing data together with a quantitative characterization of the associated uncertainty. There are several ways of characterizing uncertainties or errors in these models, which will be the primary topic of investigation for this project. One of the widely applied approaches for uncertainty analysis is Bayesian methods, which has already been applied in toxicological risk assessment for human health [1], however there is little historic use of this advance mathematical approaches within environmental safety which we will address in this project.[4]

Hence, this project provides an opportunity to develop the knowledge and capability towards applying advance mathematical approaches to solve complex biological problems via the main objectives below:
1. Exploring, evaluating, and summarizing the relevant literature surrounding the use of Bayesian methods in Physiology based kinetic (PBK) models of aquatic organisms.
2. Proposing a framework with some examples [5, 6] and proving an R implementation [6].
3. Generalisation of such Bayesian approach within models of increasing complexity. Where possible, implementation and evaluation of multi-compartmental models in this space along with uncertainty and sensitivity analysis will be considered.

It should be noted that these objectives are not set in stone, being its development up for reconsideration based on intermediate findings. Ultimately, results from this project may be incorporated and used in business case studies to drive regulatory change towards acceptance of these modelling approaches, supporting transition towards non-animal approaches within safety assessment.

Closed.  Application deadline: 17 February 2023

What you will do:
- Develop, devise and research algorithms and software to enhance Riverlane’s capabilities, contributing to one or more projects that are core to Riverlane’s goals
- Discuss ideas with colleagues and communicate work in the form of presentations and reports
- Develop an understanding of quantum computers and their industrial applications

For more information and to apply, please visit our website: https://www.riverlane.com/jobs/internships

Requirements
- A current student studying for a master’s degree (including the final year of an integrated undergraduate and master’s degree) 
- Proven ability in computational and/or theoretical work
- Experience with at least one programming language
- Excellent critical thinking and problem-solving ability
- Strong communication skills, both written and verbal
- Ability to take initiative and to work well as part of a team
- An interest in quantum computing (extensive knowledge or experience is not required)

VetCT was established in 2009, headquartered in Cambridge (UK), with a mission to improve the quality and efficiency of veterinary care. VetCT owns a large high-quality research patient database of animal CT scans, radiographs, MRI, and is currently developing artificial intelligence (AI) tools to improve workflows and clinical diagnosis for veterinary patients.

The rapid development of deep learning techniques in recent years enables the increasing utilisation of computer-aided detection (CAD) tools in assisting radiologists with their workflows and clinical diagnosis. In contrast to human medicine, the use of CAD and AI with veterinary studies is minimal and much less explored with its power yet to be exploited.

Anglo Scientific:
https://www.angloscientific.com/

Dazlus
https://www.dazlus.com/

Sai Bot
https://www.mobiliar.ch/die-mobiliar/nachhaltigkeit-engagement/das-gesellschaftsengagement-der-mobiliar/kunst-und-kultur/ausstellungen-und-fuehrungen/kunstausstellung-tobias-gutmann-und-sai-bot
https://www.under-dogs.net/blogs/exhibitions/tobias-gutmann-solo-exhibition

Dr Lisu Su (Chair and CEO at AMD) recently delivered an opening presentation at ISSCC 2023 titled “Innovation For the Next Decade of Compute Efficiency”. In her talk she discussed current compute performance trends in High Performance Computing (HPC) and highlighted that our biggest challenge to sensible and sustainable improvements remains system compute efficiency (performance per watt), especially when considered against the goal of building Zettascale (1024 FLOPS) systems.

Her high level analysis relied heavily on data provided by a single measure of performance, floating point operations per second (FLOPS) as measured by the LINPACK benchmark. However, the HPC community regularly debates the topic of useful performance measures and LINPACK is generally considered overly simplistic, and at worst unhelpful, as it can be a poor indicator of performance in real scientific applications.

Given this disconnect between system and application performance, it is not unreasonable to consider that the system compute efficiency story from an application perspective may be different, leaving the door open for an evidence based analysis of compute efficiency between multiple generations of system hardware.

By the summer of 2023, the Cambridge Service for Data-Driven Discovery (CSD3), operated by the University’s Research Computing Services (RCS), will be home to four generations of Intel based HPC systems. It has been observed that with the integration of each new system performance is “improved” (FLOPS) but at the expense of ever expanded power and cooling envelopes. Therefore, this situation presents a unique opportunity to evaluate the inter-generational system compute efficiency, beyond FLOPS, using a standardised application-based benchmarking suite to frame the discussion in terms of real world scientific application performance.

The student will undertake a short literature review to gain a foothold in the broad discussion around energy efficiency in HPC and receive access, orientation and time to become familiar with the available systems.

This will lead into co-designing an experiment with the necessary telemetry collection which will be able to sufficiently describe and visualise the compute efficiency of each system such that an inter-generational comparison can be conducted. The student will have significant input and ownership at this point and will have the opportunity to develop the project in an interesting direction.

Expected outputs from this project are a short technical report with a target audience of members of the Cambridge Open Zettascale Lab including stakeholders from Intel and Dell EMC. Along with digital assets such as, but not limited to, the experimental output dataset, scripting and code to allow further independent analysis and reproducibility.

There is scope for extra-project activities such as touring data hall 1 at the West Cambridge Data Centre (WCDC) which houses all parts of CSD3 as well as engaging with our internal seminar series and knowledge sharing opportunities.