Physics World Weekly Podcast Data-intensive PhDs at LIV.INNO prepare students for careers outside of academia
Oct 17, 2024
Carsten Welsch, an accelerator physicist and director of LIV.INNO, and Andreea Font, a computational astrophysicist, dive into the unique PhD training offered at LIV.INNO. They discuss the importance of equipping students with skills in high-performance computing and machine learning for diverse careers beyond academia. The duo highlights the significance of industry placements and interdisciplinary collaborations, especially in applying data science to fields like healthcare and agriculture. They also touch upon the evolving role of AI in education, emphasizing the need for critical analysis of information.
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PhD Purpose and Key Ingredients
- A PhD's main purpose is demonstrating independent research abilities, requiring significant skills development beyond a first degree.
- Ideal PhD training should consider diverse career paths, necessitating an academic environment, access to research infrastructure, and industry collaboration.
Data Abundance in Astrophysics
- Astrophysics generates vast amounts of data from observations and simulations, like the Euclid survey, which sends 100 gigabytes nightly.
- Machine learning helps classify and categorize millions of galaxies and fast-moving objects within this multidimensional data.
Cosmology and Agriculture
- A student used cosmological simulations, dealing with sparsely populated data like luminous galaxies, to understand dark matter.
- Similar statistical methods applied to incomplete Earth observation data improved landscaping observations with agricultural applications.
