Negotiating your first BME Job: Do's and don'ts in academia, private sector and government
Bibliographic record
Abstract
This session is intended to prepare current bioengineering students and post-doctoral fellows, getting them in the right shape to apply, negotiate and succeed in getting their first job in industry or academia. Tips on putting together the appropriate CV, preparing your portfolio and getting ready for the interview will be covered by the invited speakers. The panel will consist of representatives from academia and the private sector, as well as government and regulators. This session aims to give you some all-round pointers on the dos and donts towards choosing, applying, attending an interview and negotiating your future position as a young biomedical engineer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".