Klaynos

So I know a good few people with PhDs, myself included. Mostly physics but some biology and at lease one languages... Of those the vast majority no long work in academia. Some did postdocs, some did not. Doing a PhD isn't just about learning/researching your topic it is about developing research skills and toolsets. I would say people fall into a few different sectors, technical software companies (mostly modelling software), academia (the minority), research for commercial companies, telecoms companies and working for government agencies. Hardly anyone works on a topic closely or even loosely related to their PhD. Some jobs people do include software engineering, technical sales, technical after sales support, translating between technical teams working in different languages, research, hardware development, product development (both physical and digital), fault modelling, data science in various forms etc... I don't talk much about my work on here, I joined my organisation at a graduate level with a PhD, not unlike many of my friends, 8.5 years ago. Compared to others with a master's who joined at a similar time I'm more senior than them now. To the point where I've chosen to not manage people and concentrate on research. I would say that that is not atypical for people with a PhD, join the same and out pace them in a couple of years. When working with people you can normally tell who went through a PhD by the speed at which they onboard with a project, the rapidity of generating and dismissing ideas etc... It's surprisingly noticeable even when dealing with people of similar experience and time with the organisation. I'm very glad I'm no longer in academia. My contemparairies have less job security and far more pressure from their colleagues for few benefits.

I agree. It's a first step. Which all it might show is that given a large number of cyclical events some well correlate well.

First you need to show a correlation. You need to actually plot the data together and show a statistically significant correlation. Then we can start talking about causes. What you've done here is state something you appear to have cherry picked and then made up a story you like. It's also always worth noting that correlation is not causation.

Humans are not good measurement systems. There are many layers of processing that are done which are complicated and not well understood. Even what seem like simple things are complicated when you investigate further. Mirror therapy is a good example of how the human mind can be trivially tricked, https://en.m.wikipedia.org/wiki/Mirror_therapy.

The way things are going towards cars broadcasting data, you might not need to rely on motion detection.

Famous person says X. Famous person says Y. Famous person is famous for Y. Sometime later X is shown to be wrong. Headline: "famous person was wrong". It's just lazy and annoying. Even if Y was"wrong" if it allowed for the thinking that resulted in what we know now it was valuable. Knowledge changes and develops. Most new ideas which most people (even the clever ones) say will be shown to be wrong in 200 years. For most new ideas, the time to being shown to be wrong is minutes.

https://web.archive.org/web/*/www.scienceforums.net

A theoretical treatise in modern physics would be mathematical. What this reads as; "I've made some stuff up and want someone to do all the hard work".

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As someone who recruits graduate and postgraduate scientists and software engineers, I would look favourably on a mathematics background. But that's because a lot of the tasks we solve are mathematical in nature. I don't think you should have much of an issue applying your skills, you may find you need to do more background reading than your peers who have a compsci background.

Kerbal space program?

One of the best "I'm just a layman" type threads I've seen in a long while. All should be commended. ALine, you might enjoy having a look at the mathematical field of mechanics, specifically kinematics.

Your question doesn't really have much meaning. Photons do not strongly interact with each other, so the answer is probably, no.

What your describing is physical models. So trivially, yes, depending on accuracy and ignoring your example of earthquakes. To give an example slightly closer to earthquakes, then numerical weather forecasting?

I'm not sure what you're talking about with the magnetic field. But, the pilots turn on and off the seatbelt light for turbulence based on a few things, including, other pilot reports, forecast wind conditions (i.e turbulence whilst entering or leaving a jet), forecast cat and convection and their nose weather radar.

The problem with trying to absorb IR is that the thing you're trying to absorb it with is normally about the same temperature and is therefore also radiating IR. It makes things tricky and not very efficient. Bolometers for IR, for example, are normally actively cooled.

We've had a discussion on this before. I suspect this want true for traditional diesel Vs electric due to the maximum output for the two engine types.

In entanglement (or superposition more generally) there is no information transfer. Causality is maintained. It's a pretty common misconception that there is information exchange, often due to sloppy pros in popsci articles.

As to the importance of mathematics in modern science. It's the language and the framework that the physical sciences are built. Repeatable predictions of physical measurements are fundemental to science, to do that accurately you need mathematics. "If I let go of this ball it will drop" is not as accurate as "if I let go of this ball, given the local gravity, air resistance and distance to the floor it will hit the ground in 7 seconds". You can then measure the 7 seconds and see if, given the relavent errors, the values are consistent with each other. As to the probability, even if it's very very very small, we should again refer to the great man (Douglas Adams),

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Please don't take this as a negative on you. I want you to make the best (whichever that is) informed decision for you. None of your answer fills me with confidence. No, I didn't mean a software company. There's a distinct difference between someone who writes software and someone who is a software engineer (which would normally also include writing software). What you then go on to talk isn't quality assurance in the software engineering context. You wouldn't have scored highly. But that's ok at this stage, as long as you know that you're writing software rather than engineering then you can make an informed decision. Given your answer a follow up might be something along the lines of "what process did you go through to decide your database schema?"

I don't know much about the US system but the team I manage does include software engineers. You say you'd be working as one, do you really mean you'd be working as a software engineer or would you be doing programming for a company who don't really know what software engineering is? That could make a big difference to you when you come to get your next job. A pretty simple interview question for an entry level software engineer might be something like "what importance do you place on quality assurance and what tools and practices have you employed to demonstrate this in a particular project?" I'd suggest you need to think about this role, it's prospects both in the company and what you might want to do career wise next, after collage. You might also be interested to know that for university graduates we look at their a-levels as well (equivalent age to high school in the USA).

Part of the reduction in brightness is due to how lumens are defined, humans are more sensitive to the bluer end of the spectrum and the scale is weighted. So the same energy coming out the front actually changes bassed on colour temperature of the light. There are other factors like the thickness and types of coatings used to on the LEDs to get a white spectrum out. They're getting better at that though (the Samsung options especially it seems). I carry an astrolux S1 with an xpl hi most days. The lower cri isn't a big problem for the most part. But it might be something worth thinking about in the context of the thread. Next time I have to think about kitchen lighting I'll take it into account. I find the led lights ive got at the moment not great for cooking meat.

Warmer colour temperature (actually cooler in terms of the number, think below 5000 K) tend to give better colour rendition to the human eye. There is also some evidence that they are less straining on the eye. In the torch (flashlight).community cool white (above about 6000 K) are normally met with derision. 4000 K is pretty comme and gives a good mid-afternoon mid latitudes kind of light. If you're really interested in colour rendering you need to look at colour rendering index (CRI). It goes from 0 to 100 with 100 being close to sunlight colour rendering. Most decent LEDs will give you 70, it's not too hard to get up around 95. Nichia and Samsung make some good high cri LEDs. In my torches I can see a difference between 5000 K at 70 cri and at 95 cri.

In academia that's probably true. There just aren't the jobs or funding. In general it's not true though. I know people who work as scientists who were certainly not the top students. Some are better at the science than others. A science degree also gives you some great transferable skills. Many of my contemporaries are doing some interesting things which are often tangentially technical (e.g. finance director for the technical wing of a company, patent lawyer etc...) In academia that's probably true. There just aren't the jobs or funding. In general it's not true though. I know people who work as scientists who were certainly not the top students. Some are better at the science than others. A science degree also gives you some great transferable skills. Many of my contemporaries are doing some interesting things which are often tangentially technical (e.g. finance director for the technical wing of a company, patent lawyer etc...)