DNA translocation through nanopores can be studied by applying a voltage and measuring the current - 'resistive pulse sensing'. In order to increase the resolution of such systems nanopores in 2D membranes could be used - however these are inherently noisy. With many terabytes of noisy data, a trained CNN could be used to detangle the translocations from the noise.

**Python****k-means Clustering****Neural Network****Signal Processing**

Bayesian statistics is used to determine a favourable model for the charge flow across defects in graphene. Following this, R is used to perform non-linear regression to fit to experimental data and extract fitting statistics.

**Python****R****Nonlinear Regression****Bayesian Statistics**

Effectively imaging features on the nanoscale requires the use of both powerful microscopes and image processing techniques. An example of such is a transmission electron microscope (TEM), which achieves some of the highest resolutions possible. Given the high resolution, lens aberrations can become limiting - image processing can be used to remove these aberrations and retrieve a higher resolution image.

**Python****Image Processing****Electron Wavefunctions**

As a protected species, knowledge of the extent and distribution of habitats of the Great Crested Newts (GCNs) is of paramount importance within both the rail and construction sectors. Using data sourced from Network Rail, I apply an adjusted 10 point scale (Oldham et. al.) to determine the likelihood of areas being occupied by GCNs - alleviating the need for costly site visits.

**R****Logistic Regression****Naive Bayes**

Perforating graphene has many applications ranging from ionic transport to single-molecule sensing. In particular, we are interesting in displacing a sufficient number of carbon atoms from graphene to create 5+ nm pores allowing the passage of DNA through such pores to be sensed.

**Labview****Nanopores**

Fetching data from a database, analysing and storing values in the cloud is achieved mostly using Java, Matlab and Python.

**Origin****Matlab****Python****SQL**

Managing large amounts of data generated from experiments is a critical part my workflow. Knowing where this data is stored and storing the analysis values saves much hassle and time.

**SQL****Azure Cloud****Java****Matlab**