Industrial collaborations and problem solving

Reseacher Susana Direito

MSc Science Communication and Public Engagement students Anna Purdue, Hanyue Sun and Jiazhuo Lin visited us recently and interviewed School of Physics and Astronomy Dr Susana Direito about her research and industrial collaborations.

Tell us about your research

I have been working with Edinburgh Complex Fluids Partnership (ECFP) for three years, and have also been a researcher for the National Biofilms Innovation Center (NBIC) for two years. I am a bit different from the other researchers because my main focus is based on industrial projects. Companies will come to us with a problem and we will try to solve it, so it works a bit like a consultancy. Continue reading “Industrial collaborations and problem solving”

Are you tempted to teach?

Pupils and teacher sitting round a table

Find out about year 3 student Maxim Oweyssi’s experience undertaking a teach physics internship in London through The Ogden Trust.

As far as my internship goes, if someone asked me a few years ago, whether I see myself teaching children, my answer would be a decisive no: “because I want to do research” I would say. However, after having the chance to prepare and teach lessons on my own, I have to admit that there is something intriguing about the prospect of becoming a teacher. Putting aside the altruistic aspect of contributing towards the knowledge of future generations, I found the actual work fulfilling. There is something immensely satisfying about that “aha” moment when your students finally understand a new concept – they have that Archimedean eureka look in their eyes.

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Machine Learning for particle physics

Student Brendan Martin

Brendan Martin is currently in year 4 of the MPhys Mathematical Physics degree.  He completed a Career Development Summer Project in machine learning.

I worked in the area of machine learning for particle physics. Machine Learning can be an extremely useful tool for analysing data from experiments – in classifying particles or identifying interesting event topologies, for example. Designing accurate, computationally cheap algorithms is therefore hugely important. Under the supervision of Prof Luigi Del Debbio, I investigated the relationship between the bias, variance and noise of a given data set using a deep neural network as an estimator. I gained insight into the fascinating, quickly developing field of machine learning whilst simultaneously improving my programming skills.

Continue reading “Machine Learning for particle physics”